{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1MTl4yEAS2Lp"
   },
   "source": [
    "### Build an MLP for ECG Signal Classification (5 classes)  - Keras\n",
    "This is the implementation of an MLP for classifying the ECG signals. <br>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "\u001b[?25hBuilding wheels for collected packages: grpcio\n",
      "  Building wheel for grpcio (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for grpcio: filename=grpcio-1.59.2-cp310-cp310-macosx_10_10_x86_64.whl size=4302746 sha256=34c3953feb909431a353ae5cfd40a3f5c4b7476b39badf55a04c33b017338510\n",
      "  Stored in directory: /Users/antoniogondim/Library/Caches/pip/wheels/c2/bb/8f/cf5945218a6e1a9fd8cff696ee5d0ab20ae6fac6b031347276\n",
      "Successfully built grpcio\n",
      "Installing collected packages: libclang, flatbuffers, termcolor, tensorflow-io-gcs-filesystem, tensorflow-estimator, tensorboard-data-server, rsa, protobuf, opt-einsum, oauthlib, ml-dtypes, keras, grpcio, google-pasta, gast, cachetools, astunparse, absl-py, requests-oauthlib, google-auth, google-auth-oauthlib, tensorboard, tensorflow\n",
      "Successfully installed absl-py-2.0.0 astunparse-1.6.3 cachetools-5.3.2 flatbuffers-23.5.26 gast-0.5.4 google-auth-2.23.4 google-auth-oauthlib-1.0.0 google-pasta-0.2.0 grpcio-1.59.2 keras-2.14.0 libclang-16.0.6 ml-dtypes-0.2.0 oauthlib-3.2.2 opt-einsum-3.3.0 protobuf-4.25.0 requests-oauthlib-1.3.1 rsa-4.9 tensorboard-2.14.1 tensorboard-data-server-0.7.2 tensorflow-2.14.0 tensorflow-estimator-2.14.0 tensorflow-io-gcs-filesystem-0.34.0 termcolor-2.3.0\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install tensorflow\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.14.0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "SOxMxEdsS-Au"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.optimizers import Adamax\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython import display\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kQOrBDYmOZjK"
   },
   "source": [
    "### Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "RsFnMl7ROZjK"
   },
   "outputs": [],
   "source": [
    "X=pd.read_csv('ECG_dataX.csv')\n",
    "Y=pd.read_csv('ECG_dataY.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.659459</td>\n",
       "      <td>0.186486</td>\n",
       "      <td>0.070270</td>\n",
       "      <td>0.070270</td>\n",
       "      <td>0.059459</td>\n",
       "      <td>0.056757</td>\n",
       "      <td>0.043243</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.045946</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.925414</td>\n",
       "      <td>0.665746</td>\n",
       "      <td>0.541436</td>\n",
       "      <td>0.276243</td>\n",
       "      <td>0.196133</td>\n",
       "      <td>0.077348</td>\n",
       "      <td>0.071823</td>\n",
       "      <td>0.060773</td>\n",
       "      <td>0.066298</td>\n",
       "      <td>0.058011</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.967136</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.830986</td>\n",
       "      <td>0.586854</td>\n",
       "      <td>0.356808</td>\n",
       "      <td>0.248826</td>\n",
       "      <td>0.145540</td>\n",
       "      <td>0.089202</td>\n",
       "      <td>0.117371</td>\n",
       "      <td>0.150235</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 187 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5         6  \\\n",
       "0  0.977941  0.926471  0.681373  0.245098  0.154412  0.191176  0.151961   \n",
       "1  0.960114  0.863248  0.461538  0.196581  0.094017  0.125356  0.099715   \n",
       "2  1.000000  0.659459  0.186486  0.070270  0.070270  0.059459  0.056757   \n",
       "3  0.925414  0.665746  0.541436  0.276243  0.196133  0.077348  0.071823   \n",
       "4  0.967136  1.000000  0.830986  0.586854  0.356808  0.248826  0.145540   \n",
       "\n",
       "          7         8         9  ...  177  178  179  180  181  182  183  184  \\\n",
       "0  0.085784  0.058824  0.049020  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "1  0.088319  0.074074  0.082621  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "2  0.043243  0.054054  0.045946  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "3  0.060773  0.066298  0.058011  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "4  0.089202  0.117371  0.150235  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "\n",
       "   185  186  \n",
       "0  0.0  0.0  \n",
       "1  0.0  0.0  \n",
       "2  0.0  0.0  \n",
       "3  0.0  0.0  \n",
       "4  0.0  0.0  \n",
       "\n",
       "[5 rows x 187 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('ECG_dataX.csv').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   class_label\n",
       "0            0\n",
       "1            0\n",
       "2            0\n",
       "3            0\n",
       "4            0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('ECG_dataY.csv').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6t4WUhbDOZjL",
    "outputId": "f6a88fc0-d45f-46ca-cbc9-7564214ca087"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3841, 187)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#convert dataframe to numpy array\n",
    "X=X.values\n",
    "X.shape\n",
    "X1=X.copy()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VwmqQyBZOZjM",
    "outputId": "75a5187c-a678-40bb-9200-974bb5d188b5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3841, 1)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#convert dataframe to numpy array\n",
    "Y=Y.values\n",
    "Y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9P_7x-_oOZjM",
    "outputId": "94c3c02f-6cc7-4e7b-833b-1f47798a384b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3841,)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reshape Y into a 1D array\n",
    "Y=Y.reshape(-1)\n",
    "Y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 317
    },
    "id": "wYaJ9_X3QDPI",
    "outputId": "bacb0867-25e6-4150-ab77-a4b6264346f6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([800.,   0., 800.,   0.,   0., 800.,   0., 641.,   0., 800.]),\n",
       " array([0. , 0.4, 0.8, 1.2, 1.6, 2. , 2.4, 2.8, 3.2, 3.6, 4. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "pLlBWI0ROzNo"
   },
   "outputs": [],
   "source": [
    "fs=125  # sampling frequency\n",
    "Ts=1/fs # sampling interval\n",
    "N=187 # the number of timepoints\n",
    "Duration=N*Ts # duration of a signal\n",
    "t=np.linspace(0, Duration-Ts, N) # array of timepoints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 745
    },
    "id": "sXzrl7-PO00f",
    "outputId": "dcd21048-a382-4e94-b805-ae1b0c7dfa1d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(5,1,constrained_layout=True, figsize=(10,10))\n",
    "for c in range(0, 5):   \n",
    "    for n in range(0, 3):\n",
    "        idx=np.random.randint(0,10)\n",
    "        ax[c].plot(t, X[Y==c][idx])        \n",
    "        ax[c].set_xlabel('time t [seconds]', fontsize=16)\n",
    "        ax[c].set_ylabel('amplitude', fontsize=16)\n",
    "    ax[c].set_title('class '+str(c), fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {
    "id": "qD6ckUN0OZjO"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=0)\n",
    "X_train, X_val, Y_train, Y_val = train_test_split(X_train, Y_train, test_size=0.1, random_state=0)\n",
    "#note: you may need to add a channel axis to the data if the network is CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2764,), (308,))"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train.shape, Y_val.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.utils import to_categorical\n",
    "\n",
    "# Convert labels to one-hot encoding\n",
    "Y_train_encoded = to_categorical(Y_train, num_classes=5)\n",
    "Y_val_encoded = to_categorical(Y_val, num_classes=5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2764, 5), (308, 5))"
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train_encoded.shape,Y_val_encoded.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0., 0., 1., ..., 0., 0., 0.]],\n",
       "\n",
       "       [[1., 0., 0., ..., 1., 0., 1.]],\n",
       "\n",
       "       [[0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "       [[1., 0., 0., ..., 0., 0., 1.]],\n",
       "\n",
       "       [[0., 0., 0., ..., 0., 0., 0.]]], dtype=float32)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_val_encoded.reshape(-1,1,308)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Activation, MaxPooling1D, LayerNormalization,Flatten\n",
    "from keras.activations import softmax\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Input\n",
    "from tensorflow.keras.layers import Add\n",
    "from keras.layers import Reshape\n",
    "from keras.layers import GlobalAveragePooling1D\n",
    "from keras.models import Model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_6\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                Output Shape                 Param #   Connected to                  \n",
      "==================================================================================================\n",
      " input_38 (InputLayer)       [(None, 187, 1)]             0         []                            \n",
      "                                                                                                  \n",
      " dense_110 (Dense)           (None, 187, 32)              64        ['input_38[0][0]']            \n",
      "                                                                                                  \n",
      " activation_108 (Activation  (None, 187, 32)              0         ['dense_110[0][0]']           \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " dense_111 (Dense)           (None, 187, 32)              1056      ['activation_108[0][0]']      \n",
      "                                                                                                  \n",
      " activation_109 (Activation  (None, 187, 32)              0         ['dense_111[0][0]']           \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " global_average_pooling1d_3  (None, 32)                   0         ['activation_109[0][0]']      \n",
      " 0 (GlobalAveragePooling1D)                                                                       \n",
      "                                                                                                  \n",
      " max_pooling1d_57 (MaxPooli  (None, 93, 32)               0         ['activation_108[0][0]']      \n",
      " ng1D)                                                                                            \n",
      "                                                                                                  \n",
      " add_39 (Add)                (None, 93, 32)               0         ['global_average_pooling1d_30[\n",
      "                                                                    0][0]',                       \n",
      "                                                                     'max_pooling1d_57[0][0]']    \n",
      "                                                                                                  \n",
      " layer_normalization_32 (La  (None, 93, 32)               64        ['add_39[0][0]']              \n",
      " yerNormalization)                                                                                \n",
      "                                                                                                  \n",
      " dense_112 (Dense)           (None, 93, 32)               1056      ['layer_normalization_32[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " activation_110 (Activation  (None, 93, 32)               0         ['dense_112[0][0]']           \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " global_average_pooling1d_3  (None, 32)                   0         ['activation_110[0][0]']      \n",
      " 1 (GlobalAveragePooling1D)                                                                       \n",
      "                                                                                                  \n",
      " max_pooling1d_58 (MaxPooli  (None, 46, 32)               0         ['add_39[0][0]']              \n",
      " ng1D)                                                                                            \n",
      "                                                                                                  \n",
      " add_40 (Add)                (None, 46, 32)               0         ['global_average_pooling1d_31[\n",
      "                                                                    0][0]',                       \n",
      "                                                                     'max_pooling1d_58[0][0]']    \n",
      "                                                                                                  \n",
      " layer_normalization_33 (La  (None, 46, 32)               64        ['add_40[0][0]']              \n",
      " yerNormalization)                                                                                \n",
      "                                                                                                  \n",
      " dense_113 (Dense)           (None, 46, 32)               1056      ['layer_normalization_33[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " activation_111 (Activation  (None, 46, 32)               0         ['dense_113[0][0]']           \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " global_average_pooling1d_3  (None, 32)                   0         ['activation_111[0][0]']      \n",
      " 2 (GlobalAveragePooling1D)                                                                       \n",
      "                                                                                                  \n",
      " max_pooling1d_59 (MaxPooli  (None, 23, 32)               0         ['add_40[0][0]']              \n",
      " ng1D)                                                                                            \n",
      "                                                                                                  \n",
      " add_41 (Add)                (None, 23, 32)               0         ['global_average_pooling1d_32[\n",
      "                                                                    0][0]',                       \n",
      "                                                                     'max_pooling1d_59[0][0]']    \n",
      "                                                                                                  \n",
      " layer_normalization_34 (La  (None, 23, 32)               64        ['add_41[0][0]']              \n",
      " yerNormalization)                                                                                \n",
      "                                                                                                  \n",
      " dense_114 (Dense)           (None, 23, 32)               1056      ['layer_normalization_34[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " activation_112 (Activation  (None, 23, 32)               0         ['dense_114[0][0]']           \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " flatten_15 (Flatten)        (None, 736)                  0         ['activation_112[0][0]']      \n",
      "                                                                                                  \n",
      " dense_115 (Dense)           (None, 5)                    3685      ['flatten_15[0][0]']          \n",
      "                                                                                                  \n",
      " activation_113 (Activation  (None, 5)                    0         ['dense_115[0][0]']           \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 8165 (31.89 KB)\n",
      "Trainable params: 8165 (31.89 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "inp=Input(shape=(187,1))\n",
    "#---1st Layer-------\n",
    "D1=Dense(32)(inp)\n",
    "A1=Activation(\"relu\")(D1)\n",
    "#---Max_Pooling------\n",
    "M1=MaxPooling1D(pool_size=2,strides=2)(A1)\n",
    "#---2nd Layer---------\n",
    "D2=Dense(32)(A1)\n",
    "A2=Activation(\"relu\")(D2)\n",
    "#GAP_A2=Flatten()(A2)\n",
    "GAP_A2 = GlobalAveragePooling1D()(A2)\n",
    "S21=Add()([GAP_A2,M1])\n",
    "#---Max_Pooling-----\n",
    "M2=MaxPooling1D(pool_size=2,strides=2)(S21)\n",
    "#---3rd Layer-------\n",
    "LN3=LayerNormalization()(S21)\n",
    "D3=Dense(32)(LN3)\n",
    "A3=Activation(\"relu\")(D3)\n",
    "GAP_A3 = GlobalAveragePooling1D()(A3)\n",
    "S32=Add()([GAP_A3,M2])\n",
    "#---Max Pooling-----\n",
    "M3=MaxPooling1D(pool_size=2,strides=2)(S32)\n",
    "#---4th Layer-------\n",
    "LN4=LayerNormalization()(S32)\n",
    "D4=Dense(32)(LN4)\n",
    "A4=Activation(\"relu\")(D4)\n",
    "GAP_A4 = GlobalAveragePooling1D()(A4)\n",
    "S43=Add()([GAP_A4,M3])\n",
    "#---5th Layer-------\n",
    "LN5=LayerNormalization()(S43)\n",
    "D5=Dense(32)(LN5)\n",
    "A5=Activation(\"relu\")(D5)\n",
    "F1=Flatten()(A5)\n",
    "#---6th Layer-------\n",
    "D6=Dense(5)(F1)\n",
    "A6=Activation(softmax)(D6)\n",
    "#----\n",
    "model=Model(inputs=inp,outputs=A6)\n",
    "model.compile(loss='sparse_categorical_crossentropy', optimizer=Adamax(learning_rate=0.001), metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(TensorShape([None, 187, 32]),\n",
       " TensorShape([None, 93, 32]),\n",
       " TensorShape([None, 187, 32]))"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A1.shape, M1.shape, A2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([None, 23, 32])"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S43.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(TensorShape([None, 5]), TensorShape([None, 5]))"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D6.shape,A6.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zyH0UvuMOZjR"
   },
   "source": [
    "### Define the MLP model  (Replace this with your network and rename the file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QTdhdBsfOZjR",
    "outputId": "dbf14998-4e16-468d-840d-3167fe69d228"
   },
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(units=256, activation='relu', input_shape=(187,)))\n",
    "model.add(Dense(units=256, activation='relu'))\n",
    "model.add(Dense(units=5, activation='softmax'))\n",
    "model.compile(loss='sparse_categorical_crossentropy', optimizer=Adamax(learning_rate=0.001), metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {
    "id": "OOXjQJ_OOZjS"
   },
   "outputs": [],
   "source": [
    "loss_train_list=[]\n",
    "loss_val_list=[]\n",
    "acc_train_list=[]\n",
    "acc_val_list=[]\n",
    "epoch_save=-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2764,)"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2764, 1, 5)"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train_encoded.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2764, 187, 1)"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train=np.array(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2764, 187, 1)"
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train=X_train.reshape(-1,187,1)\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_val=np.array(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0.        ],\n",
       "        [0.01980198],\n",
       "        [0.08712871],\n",
       "        ...,\n",
       "        [0.        ],\n",
       "        [0.        ],\n",
       "        [0.        ]],\n",
       "\n",
       "       [[0.70711297],\n",
       "        [0.38075313],\n",
       "        [0.18828452],\n",
       "        ...,\n",
       "        [0.        ],\n",
       "        [0.        ],\n",
       "        [0.        ]],\n",
       "\n",
       "       [[0.79079497],\n",
       "        [0.8242678 ],\n",
       "        [0.79288703],\n",
       "        ...,\n",
       "        [0.        ],\n",
       "        [0.        ],\n",
       "        [0.        ]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[0.98563218],\n",
       "        [0.73563218],\n",
       "        [0.27873564],\n",
       "        ...,\n",
       "        [0.        ],\n",
       "        [0.        ],\n",
       "        [0.        ]],\n",
       "\n",
       "       [[1.        ],\n",
       "        [0.92838877],\n",
       "        [0.71099746],\n",
       "        ...,\n",
       "        [0.        ],\n",
       "        [0.        ],\n",
       "        [0.        ]],\n",
       "\n",
       "       [[1.        ],\n",
       "        [0.9173913 ],\n",
       "        [0.75652176],\n",
       "        ...,\n",
       "        [0.        ],\n",
       "        [0.        ],\n",
       "        [0.        ]]])"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_val.reshape(-1,187,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((187, 1), (187,))"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train=X_train.reshape(-1,187,1)\n",
    "X_train[0].shape, X_val[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train=X_train.reshape(2764, 187,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1., 0., 0., 0., 0.]],\n",
       "\n",
       "       [[1., 0., 0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0., 0., 1.]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[0., 0., 0., 1., 0.]],\n",
       "\n",
       "       [[1., 0., 0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0., 1., 0.]]], dtype=float32)"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 0\n",
      "44/44 [==============================] - 9s 47ms/step - loss: 1.4546 - accuracy: 0.3784 - val_loss: 1.3794 - val_accuracy: 0.4253\n",
      "epoch 1\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 1.3168 - accuracy: 0.4240 - val_loss: 1.3337 - val_accuracy: 0.4481\n",
      "epoch 2\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 1.2666 - accuracy: 0.4721 - val_loss: 1.2799 - val_accuracy: 0.4935\n",
      "epoch 3\n",
      "44/44 [==============================] - 2s 43ms/step - loss: 1.2271 - accuracy: 0.4967 - val_loss: 1.2857 - val_accuracy: 0.4643\n",
      "epoch 4\n",
      "44/44 [==============================] - 2s 45ms/step - loss: 1.1918 - accuracy: 0.5185 - val_loss: 1.2214 - val_accuracy: 0.5032\n",
      "epoch 5\n",
      "44/44 [==============================] - 2s 46ms/step - loss: 1.1711 - accuracy: 0.5271 - val_loss: 1.1854 - val_accuracy: 0.5162\n",
      "epoch 6\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 1.1463 - accuracy: 0.5456 - val_loss: 1.1712 - val_accuracy: 0.5617\n",
      "epoch 7\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 1.1095 - accuracy: 0.5539 - val_loss: 1.1702 - val_accuracy: 0.5455\n",
      "epoch 8\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 1.1057 - accuracy: 0.5673 - val_loss: 1.1232 - val_accuracy: 0.5487\n",
      "epoch 9\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 1.0794 - accuracy: 0.5720 - val_loss: 1.1053 - val_accuracy: 0.5812\n",
      "epoch 10\n",
      "44/44 [==============================] - 2s 47ms/step - loss: 1.0655 - accuracy: 0.5868 - val_loss: 1.0831 - val_accuracy: 0.6331\n",
      "epoch 11\n",
      "44/44 [==============================] - 2s 50ms/step - loss: 1.0291 - accuracy: 0.6241 - val_loss: 1.0664 - val_accuracy: 0.5877\n",
      "epoch 12\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 1.0211 - accuracy: 0.6136 - val_loss: 1.0373 - val_accuracy: 0.6364\n",
      "epoch 13\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 1.0040 - accuracy: 0.6266 - val_loss: 1.0362 - val_accuracy: 0.6006\n",
      "epoch 14\n",
      "44/44 [==============================] - 2s 45ms/step - loss: 0.9868 - accuracy: 0.6245 - val_loss: 1.0048 - val_accuracy: 0.6688\n",
      "epoch 15\n",
      "44/44 [==============================] - 2s 47ms/step - loss: 0.9656 - accuracy: 0.6346 - val_loss: 0.9916 - val_accuracy: 0.6429\n",
      "epoch 16\n",
      "44/44 [==============================] - 2s 44ms/step - loss: 0.9482 - accuracy: 0.6440 - val_loss: 1.0197 - val_accuracy: 0.6364\n",
      "epoch 17\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.9472 - accuracy: 0.6386 - val_loss: 1.0190 - val_accuracy: 0.6429\n",
      "epoch 18\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.9324 - accuracy: 0.6516 - val_loss: 0.9553 - val_accuracy: 0.6526\n",
      "epoch 19\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.9052 - accuracy: 0.6722 - val_loss: 1.0075 - val_accuracy: 0.6169\n",
      "epoch 20\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.9206 - accuracy: 0.6458 - val_loss: 0.9320 - val_accuracy: 0.6623\n",
      "epoch 21\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.8994 - accuracy: 0.6610 - val_loss: 0.9209 - val_accuracy: 0.6721\n",
      "epoch 22\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.8806 - accuracy: 0.6860 - val_loss: 0.9239 - val_accuracy: 0.6623\n",
      "epoch 23\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.8740 - accuracy: 0.6795 - val_loss: 0.9212 - val_accuracy: 0.6818\n",
      "epoch 24\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.8696 - accuracy: 0.6729 - val_loss: 0.9527 - val_accuracy: 0.6364\n",
      "epoch 25\n",
      "44/44 [==============================] - 2s 51ms/step - loss: 0.8549 - accuracy: 0.6881 - val_loss: 0.8899 - val_accuracy: 0.6916\n",
      "epoch 26\n",
      "44/44 [==============================] - 2s 51ms/step - loss: 0.8380 - accuracy: 0.6939 - val_loss: 0.9315 - val_accuracy: 0.6623\n",
      "epoch 27\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.8436 - accuracy: 0.6925 - val_loss: 0.8819 - val_accuracy: 0.6786\n",
      "epoch 28\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.8323 - accuracy: 0.6979 - val_loss: 0.8916 - val_accuracy: 0.6786\n",
      "epoch 29\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.8345 - accuracy: 0.6986 - val_loss: 0.9233 - val_accuracy: 0.6721\n",
      "epoch 30\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.8137 - accuracy: 0.7077 - val_loss: 0.8993 - val_accuracy: 0.6526\n",
      "epoch 31\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.8065 - accuracy: 0.7059 - val_loss: 0.8636 - val_accuracy: 0.7143\n",
      "epoch 32\n",
      "44/44 [==============================] - 2s 43ms/step - loss: 0.7995 - accuracy: 0.7156 - val_loss: 0.8303 - val_accuracy: 0.6948\n",
      "epoch 33\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.7926 - accuracy: 0.7218 - val_loss: 0.8542 - val_accuracy: 0.6851\n",
      "epoch 34\n",
      "44/44 [==============================] - 2s 50ms/step - loss: 0.8013 - accuracy: 0.7153 - val_loss: 0.8558 - val_accuracy: 0.6916\n",
      "epoch 35\n",
      "44/44 [==============================] - 2s 48ms/step - loss: 0.7778 - accuracy: 0.7272 - val_loss: 0.8259 - val_accuracy: 0.7045\n",
      "epoch 36\n",
      "44/44 [==============================] - 2s 48ms/step - loss: 0.7803 - accuracy: 0.7250 - val_loss: 0.8294 - val_accuracy: 0.6981\n",
      "epoch 37\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.7748 - accuracy: 0.7225 - val_loss: 0.8252 - val_accuracy: 0.7045\n",
      "epoch 38\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.7561 - accuracy: 0.7344 - val_loss: 0.8154 - val_accuracy: 0.7175\n",
      "epoch 39\n",
      "44/44 [==============================] - 2s 48ms/step - loss: 0.7677 - accuracy: 0.7247 - val_loss: 0.8144 - val_accuracy: 0.7110\n",
      "epoch 40\n",
      "44/44 [==============================] - 2s 46ms/step - loss: 0.7512 - accuracy: 0.7276 - val_loss: 0.8292 - val_accuracy: 0.6981\n",
      "epoch 41\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.7464 - accuracy: 0.7384 - val_loss: 0.8302 - val_accuracy: 0.6851\n",
      "epoch 42\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.7401 - accuracy: 0.7402 - val_loss: 0.7963 - val_accuracy: 0.7208\n",
      "epoch 43\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.7380 - accuracy: 0.7446 - val_loss: 0.8270 - val_accuracy: 0.7175\n",
      "epoch 44\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.7417 - accuracy: 0.7341 - val_loss: 0.8275 - val_accuracy: 0.7045\n",
      "epoch 45\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.7164 - accuracy: 0.7522 - val_loss: 0.8610 - val_accuracy: 0.6753\n",
      "epoch 46\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.7284 - accuracy: 0.7428 - val_loss: 0.8087 - val_accuracy: 0.6851\n",
      "epoch 47\n",
      "44/44 [==============================] - 2s 43ms/step - loss: 0.7123 - accuracy: 0.7460 - val_loss: 0.7823 - val_accuracy: 0.7338\n",
      "epoch 48\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.7096 - accuracy: 0.7529 - val_loss: 0.7975 - val_accuracy: 0.7208\n",
      "epoch 49\n",
      "44/44 [==============================] - 2s 54ms/step - loss: 0.7145 - accuracy: 0.7489 - val_loss: 0.8702 - val_accuracy: 0.6753\n",
      "epoch 50\n",
      "44/44 [==============================] - 2s 55ms/step - loss: 0.7087 - accuracy: 0.7496 - val_loss: 0.7541 - val_accuracy: 0.7403\n",
      "epoch 51\n",
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      "epoch 52\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.7076 - accuracy: 0.7424 - val_loss: 0.7723 - val_accuracy: 0.7273\n",
      "epoch 53\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.6764 - accuracy: 0.7580 - val_loss: 0.7421 - val_accuracy: 0.7403\n",
      "epoch 54\n",
      "44/44 [==============================] - 2s 44ms/step - loss: 0.6768 - accuracy: 0.7670 - val_loss: 0.7429 - val_accuracy: 0.7305\n",
      "epoch 55\n",
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      "epoch 56\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.6705 - accuracy: 0.7569 - val_loss: 0.7535 - val_accuracy: 0.7175\n",
      "epoch 57\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.6779 - accuracy: 0.7605 - val_loss: 0.8322 - val_accuracy: 0.7273\n",
      "epoch 58\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.6702 - accuracy: 0.7630 - val_loss: 0.7430 - val_accuracy: 0.7338\n",
      "epoch 59\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.6742 - accuracy: 0.7565 - val_loss: 0.7816 - val_accuracy: 0.7273\n",
      "epoch 60\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.6580 - accuracy: 0.7659 - val_loss: 0.7761 - val_accuracy: 0.7110\n",
      "epoch 61\n",
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      "epoch 62\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.6508 - accuracy: 0.7681 - val_loss: 0.7391 - val_accuracy: 0.7403\n",
      "epoch 63\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.6483 - accuracy: 0.7757 - val_loss: 0.7383 - val_accuracy: 0.7338\n",
      "epoch 64\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.6561 - accuracy: 0.7666 - val_loss: 0.7701 - val_accuracy: 0.7435\n",
      "epoch 65\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.6607 - accuracy: 0.7623 - val_loss: 0.7535 - val_accuracy: 0.7273\n",
      "epoch 66\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.6469 - accuracy: 0.7739 - val_loss: 0.7404 - val_accuracy: 0.7338\n",
      "epoch 67\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.6526 - accuracy: 0.7666 - val_loss: 0.7588 - val_accuracy: 0.7143\n",
      "epoch 68\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.6390 - accuracy: 0.7732 - val_loss: 0.7211 - val_accuracy: 0.7305\n",
      "epoch 69\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.6353 - accuracy: 0.7728 - val_loss: 0.7729 - val_accuracy: 0.7370\n",
      "epoch 70\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.6565 - accuracy: 0.7699 - val_loss: 0.7425 - val_accuracy: 0.7532\n",
      "epoch 71\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.6274 - accuracy: 0.7768 - val_loss: 0.7424 - val_accuracy: 0.7370\n",
      "epoch 72\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.6315 - accuracy: 0.7739 - val_loss: 0.7822 - val_accuracy: 0.7305\n",
      "epoch 73\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.6255 - accuracy: 0.7757 - val_loss: 0.7191 - val_accuracy: 0.7500\n",
      "epoch 74\n",
      "44/44 [==============================] - 2s 50ms/step - loss: 0.6190 - accuracy: 0.7815 - val_loss: 0.7257 - val_accuracy: 0.7435\n",
      "epoch 75\n",
      "44/44 [==============================] - 2s 43ms/step - loss: 0.6256 - accuracy: 0.7815 - val_loss: 0.7464 - val_accuracy: 0.7500\n",
      "epoch 76\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.6303 - accuracy: 0.7793 - val_loss: 0.7389 - val_accuracy: 0.7532\n",
      "epoch 77\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.6252 - accuracy: 0.7721 - val_loss: 0.7982 - val_accuracy: 0.7305\n",
      "epoch 78\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.6166 - accuracy: 0.7844 - val_loss: 0.7148 - val_accuracy: 0.7403\n",
      "epoch 79\n",
      "44/44 [==============================] - 2s 49ms/step - loss: 0.6036 - accuracy: 0.7876 - val_loss: 0.7011 - val_accuracy: 0.7662\n",
      "epoch 80\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.6097 - accuracy: 0.7804 - val_loss: 0.7182 - val_accuracy: 0.7597\n",
      "epoch 81\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.6148 - accuracy: 0.7808 - val_loss: 0.7437 - val_accuracy: 0.7500\n",
      "epoch 82\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.6306 - accuracy: 0.7771 - val_loss: 0.7078 - val_accuracy: 0.7403\n",
      "epoch 83\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.5885 - accuracy: 0.7959 - val_loss: 0.7104 - val_accuracy: 0.7500\n",
      "epoch 84\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.5860 - accuracy: 0.7938 - val_loss: 0.7068 - val_accuracy: 0.7435\n",
      "epoch 85\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.5920 - accuracy: 0.7927 - val_loss: 0.7125 - val_accuracy: 0.7597\n",
      "epoch 86\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.5923 - accuracy: 0.7869 - val_loss: 0.7374 - val_accuracy: 0.7565\n",
      "epoch 87\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.5905 - accuracy: 0.7858 - val_loss: 0.6940 - val_accuracy: 0.7662\n",
      "epoch 88\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.5963 - accuracy: 0.7898 - val_loss: 0.6874 - val_accuracy: 0.7435\n",
      "epoch 89\n",
      "44/44 [==============================] - 2s 47ms/step - loss: 0.5966 - accuracy: 0.7836 - val_loss: 0.6932 - val_accuracy: 0.7565\n",
      "epoch 90\n",
      "44/44 [==============================] - 2s 50ms/step - loss: 0.5832 - accuracy: 0.7931 - val_loss: 0.6933 - val_accuracy: 0.7468\n",
      "epoch 91\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.5816 - accuracy: 0.7920 - val_loss: 0.7158 - val_accuracy: 0.7500\n",
      "epoch 92\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.5768 - accuracy: 0.7916 - val_loss: 0.7189 - val_accuracy: 0.7435\n",
      "epoch 93\n",
      "44/44 [==============================] - 2s 46ms/step - loss: 0.5752 - accuracy: 0.7978 - val_loss: 0.7559 - val_accuracy: 0.7403\n",
      "epoch 94\n",
      "44/44 [==============================] - 2s 48ms/step - loss: 0.5736 - accuracy: 0.7956 - val_loss: 0.6811 - val_accuracy: 0.7565\n",
      "epoch 95\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.5604 - accuracy: 0.8057 - val_loss: 0.6899 - val_accuracy: 0.7695\n",
      "epoch 96\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.5737 - accuracy: 0.7978 - val_loss: 0.7172 - val_accuracy: 0.7565\n",
      "epoch 97\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.5820 - accuracy: 0.7898 - val_loss: 0.7637 - val_accuracy: 0.7500\n",
      "epoch 98\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.5850 - accuracy: 0.7909 - val_loss: 0.7146 - val_accuracy: 0.7468\n",
      "epoch 99\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.5794 - accuracy: 0.7931 - val_loss: 0.6995 - val_accuracy: 0.7565\n",
      "epoch 100\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.5645 - accuracy: 0.7988 - val_loss: 0.7105 - val_accuracy: 0.7532\n",
      "epoch 101\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.5595 - accuracy: 0.7974 - val_loss: 0.6924 - val_accuracy: 0.7695\n",
      "epoch 102\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.5625 - accuracy: 0.8028 - val_loss: 0.7104 - val_accuracy: 0.7565\n",
      "epoch 103\n",
      "44/44 [==============================] - 2s 48ms/step - loss: 0.5693 - accuracy: 0.8007 - val_loss: 0.6927 - val_accuracy: 0.7468\n",
      "epoch 104\n",
      "44/44 [==============================] - 2s 51ms/step - loss: 0.5652 - accuracy: 0.7992 - val_loss: 0.7430 - val_accuracy: 0.7662\n",
      "epoch 105\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.5514 - accuracy: 0.8046 - val_loss: 0.6644 - val_accuracy: 0.7792\n",
      "epoch 106\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.5457 - accuracy: 0.8025 - val_loss: 0.7007 - val_accuracy: 0.7727\n",
      "epoch 107\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.5427 - accuracy: 0.7985 - val_loss: 0.6709 - val_accuracy: 0.7792\n",
      "epoch 108\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.5485 - accuracy: 0.8064 - val_loss: 0.6746 - val_accuracy: 0.7825\n",
      "epoch 109\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.5461 - accuracy: 0.8079 - val_loss: 0.6797 - val_accuracy: 0.7792\n",
      "epoch 110\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.5379 - accuracy: 0.8054 - val_loss: 0.6693 - val_accuracy: 0.7825\n",
      "epoch 111\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.5434 - accuracy: 0.8086 - val_loss: 0.6813 - val_accuracy: 0.7792\n",
      "epoch 112\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.5320 - accuracy: 0.8104 - val_loss: 0.6589 - val_accuracy: 0.7630\n",
      "epoch 113\n",
      "44/44 [==============================] - 2s 46ms/step - loss: 0.5377 - accuracy: 0.8126 - val_loss: 0.6816 - val_accuracy: 0.7565\n",
      "epoch 114\n",
      "44/44 [==============================] - 2s 50ms/step - loss: 0.5440 - accuracy: 0.8108 - val_loss: 0.6768 - val_accuracy: 0.7662\n",
      "epoch 115\n",
      "44/44 [==============================] - 2s 42ms/step - loss: 0.5483 - accuracy: 0.8050 - val_loss: 0.6581 - val_accuracy: 0.7792\n",
      "epoch 116\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.5494 - accuracy: 0.7988 - val_loss: 0.6740 - val_accuracy: 0.7727\n",
      "epoch 117\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.5223 - accuracy: 0.8162 - val_loss: 0.7338 - val_accuracy: 0.7565\n",
      "epoch 118\n",
      "44/44 [==============================] - 2s 45ms/step - loss: 0.5363 - accuracy: 0.8061 - val_loss: 0.6658 - val_accuracy: 0.7955\n",
      "epoch 119\n",
      "44/44 [==============================] - 2s 48ms/step - loss: 0.5198 - accuracy: 0.8119 - val_loss: 0.6590 - val_accuracy: 0.7922\n",
      "epoch 120\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.5248 - accuracy: 0.8158 - val_loss: 0.7429 - val_accuracy: 0.7500\n",
      "epoch 121\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.5386 - accuracy: 0.7999 - val_loss: 0.6942 - val_accuracy: 0.7857\n",
      "epoch 122\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.5244 - accuracy: 0.8086 - val_loss: 0.6766 - val_accuracy: 0.7792\n",
      "epoch 123\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.5411 - accuracy: 0.8115 - val_loss: 0.6550 - val_accuracy: 0.7630\n",
      "epoch 124\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.5274 - accuracy: 0.8151 - val_loss: 0.6526 - val_accuracy: 0.7727\n",
      "epoch 125\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.5070 - accuracy: 0.8216 - val_loss: 0.6526 - val_accuracy: 0.7695\n",
      "epoch 126\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.5348 - accuracy: 0.8046 - val_loss: 0.6437 - val_accuracy: 0.7922\n",
      "epoch 127\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.5137 - accuracy: 0.8180 - val_loss: 0.6537 - val_accuracy: 0.7760\n",
      "epoch 128\n",
      "44/44 [==============================] - 2s 48ms/step - loss: 0.5157 - accuracy: 0.8111 - val_loss: 0.6680 - val_accuracy: 0.7857\n",
      "epoch 129\n",
      "44/44 [==============================] - 2s 47ms/step - loss: 0.5137 - accuracy: 0.8184 - val_loss: 0.6483 - val_accuracy: 0.7792\n",
      "epoch 130\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.5187 - accuracy: 0.8173 - val_loss: 0.6670 - val_accuracy: 0.7987\n",
      "epoch 131\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.5017 - accuracy: 0.8184 - val_loss: 0.6462 - val_accuracy: 0.7760\n",
      "epoch 132\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.5067 - accuracy: 0.8187 - val_loss: 0.6596 - val_accuracy: 0.7922\n",
      "epoch 133\n",
      "44/44 [==============================] - 2s 49ms/step - loss: 0.5081 - accuracy: 0.8278 - val_loss: 0.6453 - val_accuracy: 0.7955\n",
      "epoch 134\n",
      "44/44 [==============================] - 2s 44ms/step - loss: 0.5063 - accuracy: 0.8187 - val_loss: 0.6725 - val_accuracy: 0.7727\n",
      "epoch 135\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.4956 - accuracy: 0.8191 - val_loss: 0.6608 - val_accuracy: 0.7857\n",
      "epoch 136\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.5053 - accuracy: 0.8205 - val_loss: 0.6835 - val_accuracy: 0.7695\n",
      "epoch 137\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.4946 - accuracy: 0.8256 - val_loss: 0.6435 - val_accuracy: 0.7890\n",
      "epoch 138\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.4940 - accuracy: 0.8231 - val_loss: 0.7034 - val_accuracy: 0.7532\n",
      "epoch 139\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.5064 - accuracy: 0.8187 - val_loss: 0.6652 - val_accuracy: 0.7890\n",
      "epoch 140\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.4980 - accuracy: 0.8224 - val_loss: 0.6540 - val_accuracy: 0.7792\n",
      "epoch 141\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.4948 - accuracy: 0.8187 - val_loss: 0.6290 - val_accuracy: 0.8019\n",
      "epoch 142\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.4897 - accuracy: 0.8202 - val_loss: 0.6697 - val_accuracy: 0.7727\n",
      "epoch 143\n",
      "44/44 [==============================] - 2s 42ms/step - loss: 0.4902 - accuracy: 0.8216 - val_loss: 0.6764 - val_accuracy: 0.7857\n",
      "epoch 144\n",
      "44/44 [==============================] - 2s 52ms/step - loss: 0.4897 - accuracy: 0.8263 - val_loss: 0.6564 - val_accuracy: 0.7922\n",
      "epoch 145\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.4775 - accuracy: 0.8292 - val_loss: 0.6304 - val_accuracy: 0.8052\n",
      "epoch 146\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.4787 - accuracy: 0.8267 - val_loss: 0.6680 - val_accuracy: 0.8084\n",
      "epoch 147\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.4784 - accuracy: 0.8310 - val_loss: 0.6744 - val_accuracy: 0.8117\n",
      "epoch 148\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.4907 - accuracy: 0.8173 - val_loss: 0.6786 - val_accuracy: 0.7825\n",
      "epoch 149\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.4831 - accuracy: 0.8281 - val_loss: 0.6396 - val_accuracy: 0.8052\n",
      "epoch 150\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.4739 - accuracy: 0.8292 - val_loss: 0.6829 - val_accuracy: 0.7825\n",
      "epoch 151\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.5106 - accuracy: 0.8119 - val_loss: 0.6220 - val_accuracy: 0.8019\n",
      "epoch 152\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.4742 - accuracy: 0.8263 - val_loss: 0.6287 - val_accuracy: 0.8084\n",
      "epoch 153\n",
      "44/44 [==============================] - 2s 49ms/step - loss: 0.4746 - accuracy: 0.8220 - val_loss: 0.6601 - val_accuracy: 0.7727\n",
      "epoch 154\n",
      "44/44 [==============================] - 2s 51ms/step - loss: 0.4721 - accuracy: 0.8329 - val_loss: 0.6224 - val_accuracy: 0.7987\n",
      "epoch 155\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.4879 - accuracy: 0.8256 - val_loss: 0.6622 - val_accuracy: 0.7987\n",
      "epoch 156\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.4746 - accuracy: 0.8285 - val_loss: 0.6816 - val_accuracy: 0.7857\n",
      "epoch 157\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.4802 - accuracy: 0.8278 - val_loss: 0.6452 - val_accuracy: 0.7890\n",
      "epoch 158\n",
      "44/44 [==============================] - 2s 45ms/step - loss: 0.4836 - accuracy: 0.8187 - val_loss: 0.6488 - val_accuracy: 0.7792\n",
      "epoch 159\n",
      "44/44 [==============================] - 2s 51ms/step - loss: 0.4712 - accuracy: 0.8263 - val_loss: 0.6323 - val_accuracy: 0.7955\n",
      "epoch 160\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.4771 - accuracy: 0.8245 - val_loss: 0.6349 - val_accuracy: 0.7890\n",
      "epoch 161\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.4650 - accuracy: 0.8310 - val_loss: 0.6454 - val_accuracy: 0.7955\n",
      "epoch 162\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.4611 - accuracy: 0.8347 - val_loss: 0.6539 - val_accuracy: 0.7792\n",
      "epoch 163\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.4765 - accuracy: 0.8325 - val_loss: 0.6360 - val_accuracy: 0.7955\n",
      "epoch 164\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.4706 - accuracy: 0.8368 - val_loss: 0.6265 - val_accuracy: 0.7987\n",
      "epoch 165\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.4702 - accuracy: 0.8365 - val_loss: 0.6528 - val_accuracy: 0.7987\n",
      "epoch 166\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.4736 - accuracy: 0.8274 - val_loss: 0.6358 - val_accuracy: 0.7955\n",
      "epoch 167\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.4578 - accuracy: 0.8376 - val_loss: 0.6367 - val_accuracy: 0.7792\n",
      "epoch 168\n",
      "44/44 [==============================] - 2s 42ms/step - loss: 0.4532 - accuracy: 0.8423 - val_loss: 0.6287 - val_accuracy: 0.7987\n",
      "epoch 169\n",
      "44/44 [==============================] - 2s 53ms/step - loss: 0.4591 - accuracy: 0.8347 - val_loss: 0.6137 - val_accuracy: 0.8052\n",
      "epoch 170\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.4655 - accuracy: 0.8314 - val_loss: 0.6233 - val_accuracy: 0.7987\n",
      "epoch 171\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.4698 - accuracy: 0.8307 - val_loss: 0.6081 - val_accuracy: 0.8019\n",
      "epoch 172\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.4623 - accuracy: 0.8339 - val_loss: 0.6303 - val_accuracy: 0.8019\n",
      "epoch 173\n",
      "44/44 [==============================] - 2s 44ms/step - loss: 0.4594 - accuracy: 0.8339 - val_loss: 0.7067 - val_accuracy: 0.7792\n",
      "epoch 174\n",
      "44/44 [==============================] - 2s 50ms/step - loss: 0.4589 - accuracy: 0.8372 - val_loss: 0.6159 - val_accuracy: 0.8084\n",
      "epoch 175\n",
      "44/44 [==============================] - 2s 48ms/step - loss: 0.4379 - accuracy: 0.8448 - val_loss: 0.6428 - val_accuracy: 0.7955\n",
      "epoch 176\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.4582 - accuracy: 0.8365 - val_loss: 0.6172 - val_accuracy: 0.8084\n",
      "epoch 177\n",
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      "epoch 178\n",
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      "epoch 179\n",
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      "epoch 180\n",
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      "epoch 181\n",
      "44/44 [==============================] - 2s 42ms/step - loss: 0.4363 - accuracy: 0.8423 - val_loss: 0.6394 - val_accuracy: 0.7955\n",
      "epoch 182\n",
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      "epoch 183\n",
      "44/44 [==============================] - 2s 47ms/step - loss: 0.4335 - accuracy: 0.8404 - val_loss: 0.6362 - val_accuracy: 0.8019\n",
      "epoch 184\n",
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      "epoch 185\n",
      "44/44 [==============================] - 2s 51ms/step - loss: 0.4349 - accuracy: 0.8433 - val_loss: 0.6243 - val_accuracy: 0.8149\n",
      "epoch 186\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.4431 - accuracy: 0.8401 - val_loss: 0.6030 - val_accuracy: 0.8149\n",
      "epoch 187\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.4242 - accuracy: 0.8513 - val_loss: 0.6133 - val_accuracy: 0.7890\n",
      "epoch 188\n",
      "44/44 [==============================] - 2s 42ms/step - loss: 0.4361 - accuracy: 0.8423 - val_loss: 0.6115 - val_accuracy: 0.8052\n",
      "epoch 189\n",
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      "epoch 190\n",
      "44/44 [==============================] - 2s 43ms/step - loss: 0.4267 - accuracy: 0.8491 - val_loss: 0.6363 - val_accuracy: 0.7890\n",
      "epoch 191\n",
      "44/44 [==============================] - 2s 44ms/step - loss: 0.4348 - accuracy: 0.8466 - val_loss: 0.5947 - val_accuracy: 0.8117\n",
      "epoch 192\n",
      "44/44 [==============================] - 2s 44ms/step - loss: 0.4479 - accuracy: 0.8415 - val_loss: 0.6132 - val_accuracy: 0.8149\n",
      "epoch 193\n",
      "44/44 [==============================] - 2s 45ms/step - loss: 0.4332 - accuracy: 0.8452 - val_loss: 0.5847 - val_accuracy: 0.8117\n",
      "epoch 194\n",
      "44/44 [==============================] - 2s 52ms/step - loss: 0.4279 - accuracy: 0.8509 - val_loss: 0.6366 - val_accuracy: 0.7987\n",
      "epoch 195\n",
      "44/44 [==============================] - 2s 43ms/step - loss: 0.4249 - accuracy: 0.8462 - val_loss: 0.6041 - val_accuracy: 0.8084\n",
      "epoch 196\n",
      "44/44 [==============================] - 2s 50ms/step - loss: 0.4227 - accuracy: 0.8495 - val_loss: 0.5832 - val_accuracy: 0.8247\n",
      "epoch 197\n",
      "44/44 [==============================] - 2s 53ms/step - loss: 0.4220 - accuracy: 0.8506 - val_loss: 0.6077 - val_accuracy: 0.8019\n",
      "epoch 198\n",
      "44/44 [==============================] - 2s 43ms/step - loss: 0.4249 - accuracy: 0.8491 - val_loss: 0.6182 - val_accuracy: 0.8182\n",
      "epoch 199\n",
      "44/44 [==============================] - 2s 42ms/step - loss: 0.4149 - accuracy: 0.8524 - val_loss: 0.6452 - val_accuracy: 0.7922\n",
      "epoch 200\n",
      "44/44 [==============================] - 2s 42ms/step - loss: 0.4240 - accuracy: 0.8509 - val_loss: 0.6150 - val_accuracy: 0.8182\n",
      "epoch 201\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.4182 - accuracy: 0.8473 - val_loss: 0.6022 - val_accuracy: 0.8052\n",
      "epoch 202\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.4175 - accuracy: 0.8535 - val_loss: 0.5889 - val_accuracy: 0.8247\n",
      "epoch 203\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.4110 - accuracy: 0.8556 - val_loss: 0.5939 - val_accuracy: 0.8247\n",
      "epoch 204\n",
      "44/44 [==============================] - 2s 43ms/step - loss: 0.4157 - accuracy: 0.8535 - val_loss: 0.5853 - val_accuracy: 0.8247\n",
      "epoch 205\n",
      "44/44 [==============================] - 2s 46ms/step - loss: 0.4099 - accuracy: 0.8560 - val_loss: 0.5898 - val_accuracy: 0.8214\n",
      "epoch 206\n",
      "44/44 [==============================] - 2s 54ms/step - loss: 0.4092 - accuracy: 0.8520 - val_loss: 0.6395 - val_accuracy: 0.8052\n",
      "epoch 207\n",
      "44/44 [==============================] - 2s 45ms/step - loss: 0.4188 - accuracy: 0.8535 - val_loss: 0.6286 - val_accuracy: 0.7955\n",
      "epoch 208\n",
      "44/44 [==============================] - 2s 42ms/step - loss: 0.4169 - accuracy: 0.8452 - val_loss: 0.5841 - val_accuracy: 0.8117\n",
      "epoch 209\n",
      "44/44 [==============================] - 2s 45ms/step - loss: 0.4135 - accuracy: 0.8459 - val_loss: 0.6563 - val_accuracy: 0.7955\n",
      "epoch 210\n",
      "44/44 [==============================] - 2s 53ms/step - loss: 0.4246 - accuracy: 0.8473 - val_loss: 0.5948 - val_accuracy: 0.8052\n",
      "epoch 211\n",
      "44/44 [==============================] - 2s 46ms/step - loss: 0.4184 - accuracy: 0.8524 - val_loss: 0.5962 - val_accuracy: 0.8182\n",
      "epoch 212\n",
      "44/44 [==============================] - 2s 42ms/step - loss: 0.4243 - accuracy: 0.8484 - val_loss: 0.5787 - val_accuracy: 0.8214\n",
      "epoch 213\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.4245 - accuracy: 0.8484 - val_loss: 0.6484 - val_accuracy: 0.7922\n",
      "epoch 214\n",
      "44/44 [==============================] - 2s 42ms/step - loss: 0.4144 - accuracy: 0.8466 - val_loss: 0.5983 - val_accuracy: 0.8084\n",
      "epoch 215\n",
      "44/44 [==============================] - 2s 43ms/step - loss: 0.4055 - accuracy: 0.8582 - val_loss: 0.6064 - val_accuracy: 0.8019\n",
      "epoch 216\n",
      "44/44 [==============================] - 2s 53ms/step - loss: 0.4173 - accuracy: 0.8488 - val_loss: 0.6415 - val_accuracy: 0.7857\n",
      "epoch 217\n",
      "44/44 [==============================] - 2s 44ms/step - loss: 0.4293 - accuracy: 0.8441 - val_loss: 0.5976 - val_accuracy: 0.7987\n",
      "epoch 218\n",
      "44/44 [==============================] - 2s 45ms/step - loss: 0.4090 - accuracy: 0.8535 - val_loss: 0.5955 - val_accuracy: 0.7955\n",
      "epoch 219\n",
      "44/44 [==============================] - 2s 45ms/step - loss: 0.3974 - accuracy: 0.8585 - val_loss: 0.5842 - val_accuracy: 0.8084\n",
      "epoch 220\n",
      "44/44 [==============================] - 2s 49ms/step - loss: 0.4096 - accuracy: 0.8531 - val_loss: 0.5842 - val_accuracy: 0.8084\n",
      "epoch 221\n",
      "44/44 [==============================] - 2s 46ms/step - loss: 0.3953 - accuracy: 0.8575 - val_loss: 0.5891 - val_accuracy: 0.8117\n",
      "epoch 222\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.3980 - accuracy: 0.8560 - val_loss: 0.5976 - val_accuracy: 0.8117\n",
      "epoch 223\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.3949 - accuracy: 0.8549 - val_loss: 0.5991 - val_accuracy: 0.8084\n",
      "epoch 224\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.3970 - accuracy: 0.8611 - val_loss: 0.6190 - val_accuracy: 0.7987\n",
      "epoch 225\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.3894 - accuracy: 0.8600 - val_loss: 0.6007 - val_accuracy: 0.7987\n",
      "epoch 226\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.4058 - accuracy: 0.8603 - val_loss: 0.6062 - val_accuracy: 0.8149\n",
      "epoch 227\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.3899 - accuracy: 0.8636 - val_loss: 0.5897 - val_accuracy: 0.8214\n",
      "epoch 228\n",
      "44/44 [==============================] - 2s 48ms/step - loss: 0.3971 - accuracy: 0.8549 - val_loss: 0.6275 - val_accuracy: 0.7987\n",
      "epoch 229\n",
      "44/44 [==============================] - 2s 45ms/step - loss: 0.3961 - accuracy: 0.8589 - val_loss: 0.6057 - val_accuracy: 0.8214\n",
      "epoch 230\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.3962 - accuracy: 0.8553 - val_loss: 0.5902 - val_accuracy: 0.8052\n",
      "epoch 231\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.3882 - accuracy: 0.8607 - val_loss: 0.6235 - val_accuracy: 0.7987\n",
      "epoch 232\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.3884 - accuracy: 0.8611 - val_loss: 0.6135 - val_accuracy: 0.8052\n",
      "epoch 233\n",
      "44/44 [==============================] - 2s 49ms/step - loss: 0.3949 - accuracy: 0.8636 - val_loss: 0.5708 - val_accuracy: 0.8247\n",
      "epoch 234\n",
      "44/44 [==============================] - 2s 46ms/step - loss: 0.3923 - accuracy: 0.8535 - val_loss: 0.5843 - val_accuracy: 0.8182\n",
      "epoch 235\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.3838 - accuracy: 0.8640 - val_loss: 0.5893 - val_accuracy: 0.8182\n",
      "epoch 236\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.3826 - accuracy: 0.8665 - val_loss: 0.5886 - val_accuracy: 0.8279\n",
      "epoch 237\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.3946 - accuracy: 0.8564 - val_loss: 0.5862 - val_accuracy: 0.8117\n",
      "epoch 238\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.3947 - accuracy: 0.8618 - val_loss: 0.5943 - val_accuracy: 0.8117\n",
      "epoch 239\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.3921 - accuracy: 0.8614 - val_loss: 0.5989 - val_accuracy: 0.8149\n",
      "epoch 240\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.3834 - accuracy: 0.8661 - val_loss: 0.5795 - val_accuracy: 0.8117\n",
      "epoch 241\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.3816 - accuracy: 0.8683 - val_loss: 0.5916 - val_accuracy: 0.8084\n",
      "epoch 242\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.3866 - accuracy: 0.8647 - val_loss: 0.5639 - val_accuracy: 0.8279\n",
      "epoch 243\n",
      "44/44 [==============================] - 2s 48ms/step - loss: 0.3775 - accuracy: 0.8654 - val_loss: 0.5987 - val_accuracy: 0.8182\n",
      "epoch 244\n",
      "44/44 [==============================] - 2s 46ms/step - loss: 0.3800 - accuracy: 0.8672 - val_loss: 0.5917 - val_accuracy: 0.8052\n",
      "epoch 245\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.3789 - accuracy: 0.8618 - val_loss: 0.5845 - val_accuracy: 0.8182\n",
      "epoch 246\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.3877 - accuracy: 0.8614 - val_loss: 0.5742 - val_accuracy: 0.8019\n",
      "epoch 247\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.3928 - accuracy: 0.8607 - val_loss: 0.6022 - val_accuracy: 0.8052\n",
      "epoch 248\n",
      "44/44 [==============================] - 2s 42ms/step - loss: 0.3923 - accuracy: 0.8600 - val_loss: 0.5952 - val_accuracy: 0.8214\n",
      "epoch 249\n",
      "44/44 [==============================] - 2s 49ms/step - loss: 0.3761 - accuracy: 0.8712 - val_loss: 0.5819 - val_accuracy: 0.8214\n",
      "epoch 250\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.3771 - accuracy: 0.8683 - val_loss: 0.6029 - val_accuracy: 0.8052\n",
      "epoch 251\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.3767 - accuracy: 0.8640 - val_loss: 0.5873 - val_accuracy: 0.8214\n",
      "epoch 252\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.3808 - accuracy: 0.8676 - val_loss: 0.6045 - val_accuracy: 0.8214\n",
      "epoch 253\n",
      "44/44 [==============================] - 2s 42ms/step - loss: 0.3790 - accuracy: 0.8611 - val_loss: 0.5596 - val_accuracy: 0.8279\n",
      "epoch 254\n",
      "44/44 [==============================] - 2s 42ms/step - loss: 0.3735 - accuracy: 0.8640 - val_loss: 0.5996 - val_accuracy: 0.8214\n",
      "epoch 255\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.3809 - accuracy: 0.8636 - val_loss: 0.5757 - val_accuracy: 0.8149\n",
      "epoch 256\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.3737 - accuracy: 0.8676 - val_loss: 0.6124 - val_accuracy: 0.8052\n",
      "epoch 257\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.3723 - accuracy: 0.8712 - val_loss: 0.5677 - val_accuracy: 0.8214\n",
      "epoch 258\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.3753 - accuracy: 0.8694 - val_loss: 0.5958 - val_accuracy: 0.8149\n",
      "epoch 259\n",
      "44/44 [==============================] - 3s 68ms/step - loss: 0.3918 - accuracy: 0.8611 - val_loss: 0.6081 - val_accuracy: 0.8084\n",
      "epoch 260\n",
      "44/44 [==============================] - 3s 78ms/step - loss: 0.3660 - accuracy: 0.8698 - val_loss: 0.5823 - val_accuracy: 0.8247\n",
      "epoch 261\n",
      "44/44 [==============================] - 3s 63ms/step - loss: 0.3634 - accuracy: 0.8705 - val_loss: 0.5620 - val_accuracy: 0.8247\n",
      "epoch 262\n",
      "44/44 [==============================] - 2s 56ms/step - loss: 0.3567 - accuracy: 0.8734 - val_loss: 0.5738 - val_accuracy: 0.8312\n",
      "epoch 263\n",
      "44/44 [==============================] - 2s 49ms/step - loss: 0.3620 - accuracy: 0.8737 - val_loss: 0.5943 - val_accuracy: 0.8019\n",
      "epoch 264\n",
      "44/44 [==============================] - 2s 44ms/step - loss: 0.3747 - accuracy: 0.8669 - val_loss: 0.5697 - val_accuracy: 0.8117\n",
      "epoch 265\n",
      "44/44 [==============================] - 2s 42ms/step - loss: 0.3657 - accuracy: 0.8730 - val_loss: 0.5743 - val_accuracy: 0.8247\n",
      "epoch 266\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.3645 - accuracy: 0.8705 - val_loss: 0.6142 - val_accuracy: 0.8084\n",
      "epoch 267\n",
      "44/44 [==============================] - 2s 42ms/step - loss: 0.3671 - accuracy: 0.8647 - val_loss: 0.5720 - val_accuracy: 0.8149\n",
      "epoch 268\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.3576 - accuracy: 0.8683 - val_loss: 0.5591 - val_accuracy: 0.8279\n",
      "epoch 269\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.3712 - accuracy: 0.8679 - val_loss: 0.5911 - val_accuracy: 0.8214\n",
      "epoch 270\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.3574 - accuracy: 0.8737 - val_loss: 0.6009 - val_accuracy: 0.8117\n",
      "epoch 271\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.3484 - accuracy: 0.8759 - val_loss: 0.5578 - val_accuracy: 0.8214\n",
      "epoch 272\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.3468 - accuracy: 0.8799 - val_loss: 0.5775 - val_accuracy: 0.8247\n",
      "epoch 273\n",
      "44/44 [==============================] - 2s 48ms/step - loss: 0.3552 - accuracy: 0.8752 - val_loss: 0.5991 - val_accuracy: 0.8149\n",
      "epoch 274\n",
      "44/44 [==============================] - 2s 50ms/step - loss: 0.3612 - accuracy: 0.8730 - val_loss: 0.5684 - val_accuracy: 0.8214\n",
      "epoch 275\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.3729 - accuracy: 0.8669 - val_loss: 0.6137 - val_accuracy: 0.8084\n",
      "epoch 276\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.3663 - accuracy: 0.8661 - val_loss: 0.6066 - val_accuracy: 0.8182\n",
      "epoch 277\n",
      "44/44 [==============================] - 2s 42ms/step - loss: 0.3676 - accuracy: 0.8672 - val_loss: 0.5870 - val_accuracy: 0.8182\n",
      "epoch 278\n",
      "44/44 [==============================] - 2s 45ms/step - loss: 0.3623 - accuracy: 0.8694 - val_loss: 0.5522 - val_accuracy: 0.8149\n",
      "epoch 279\n",
      "44/44 [==============================] - 2s 45ms/step - loss: 0.3499 - accuracy: 0.8802 - val_loss: 0.5898 - val_accuracy: 0.8214\n",
      "epoch 280\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.3532 - accuracy: 0.8723 - val_loss: 0.5880 - val_accuracy: 0.8117\n",
      "epoch 281\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.3672 - accuracy: 0.8683 - val_loss: 0.6009 - val_accuracy: 0.8084\n",
      "epoch 282\n",
      "44/44 [==============================] - 2s 42ms/step - loss: 0.3498 - accuracy: 0.8726 - val_loss: 0.5792 - val_accuracy: 0.8312\n",
      "epoch 283\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.3560 - accuracy: 0.8774 - val_loss: 0.5700 - val_accuracy: 0.8182\n",
      "epoch 284\n",
      "44/44 [==============================] - 2s 50ms/step - loss: 0.3464 - accuracy: 0.8730 - val_loss: 0.5806 - val_accuracy: 0.8084\n",
      "epoch 285\n",
      "44/44 [==============================] - 3s 57ms/step - loss: 0.3684 - accuracy: 0.8647 - val_loss: 0.5740 - val_accuracy: 0.8084\n",
      "epoch 286\n",
      "44/44 [==============================] - 3s 62ms/step - loss: 0.3496 - accuracy: 0.8737 - val_loss: 0.6077 - val_accuracy: 0.8149\n",
      "epoch 287\n",
      "44/44 [==============================] - 2s 54ms/step - loss: 0.3496 - accuracy: 0.8701 - val_loss: 0.5817 - val_accuracy: 0.8214\n",
      "epoch 288\n",
      "44/44 [==============================] - 2s 45ms/step - loss: 0.3459 - accuracy: 0.8755 - val_loss: 0.5699 - val_accuracy: 0.8279\n",
      "epoch 289\n",
      "44/44 [==============================] - 2s 44ms/step - loss: 0.3530 - accuracy: 0.8737 - val_loss: 0.5961 - val_accuracy: 0.8247\n",
      "epoch 290\n",
      "44/44 [==============================] - 2s 50ms/step - loss: 0.3598 - accuracy: 0.8676 - val_loss: 0.5690 - val_accuracy: 0.8182\n",
      "epoch 291\n",
      "44/44 [==============================] - 2s 55ms/step - loss: 0.3459 - accuracy: 0.8763 - val_loss: 0.5610 - val_accuracy: 0.8247\n",
      "epoch 292\n",
      "44/44 [==============================] - 2s 47ms/step - loss: 0.3447 - accuracy: 0.8716 - val_loss: 0.5766 - val_accuracy: 0.8214\n",
      "epoch 293\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.3515 - accuracy: 0.8716 - val_loss: 0.5641 - val_accuracy: 0.8279\n",
      "epoch 294\n",
      "44/44 [==============================] - 2s 43ms/step - loss: 0.3415 - accuracy: 0.8774 - val_loss: 0.6367 - val_accuracy: 0.7987\n",
      "epoch 295\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.3657 - accuracy: 0.8708 - val_loss: 0.5943 - val_accuracy: 0.8084\n",
      "epoch 296\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.3400 - accuracy: 0.8745 - val_loss: 0.6170 - val_accuracy: 0.8149\n",
      "epoch 297\n",
      "44/44 [==============================] - 2s 42ms/step - loss: 0.3413 - accuracy: 0.8774 - val_loss: 0.5784 - val_accuracy: 0.8247\n",
      "epoch 298\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.3376 - accuracy: 0.8821 - val_loss: 0.5642 - val_accuracy: 0.8247\n",
      "epoch 299\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.3425 - accuracy: 0.8777 - val_loss: 0.5489 - val_accuracy: 0.8279\n",
      "epoch 300\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.3304 - accuracy: 0.8824 - val_loss: 0.5679 - val_accuracy: 0.8117\n",
      "epoch 301\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.3318 - accuracy: 0.8766 - val_loss: 0.5799 - val_accuracy: 0.8247\n",
      "epoch 302\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.3377 - accuracy: 0.8824 - val_loss: 0.5528 - val_accuracy: 0.8312\n",
      "epoch 303\n",
      "44/44 [==============================] - 2s 51ms/step - loss: 0.3455 - accuracy: 0.8752 - val_loss: 0.5617 - val_accuracy: 0.8312\n",
      "epoch 304\n",
      "44/44 [==============================] - 2s 49ms/step - loss: 0.3337 - accuracy: 0.8828 - val_loss: 0.5526 - val_accuracy: 0.8279\n",
      "epoch 305\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.3421 - accuracy: 0.8799 - val_loss: 0.5564 - val_accuracy: 0.8214\n",
      "epoch 306\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.3405 - accuracy: 0.8817 - val_loss: 0.5656 - val_accuracy: 0.8214\n",
      "epoch 307\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.3254 - accuracy: 0.8839 - val_loss: 0.5967 - val_accuracy: 0.8182\n",
      "epoch 308\n",
      "44/44 [==============================] - 2s 48ms/step - loss: 0.3391 - accuracy: 0.8748 - val_loss: 0.5668 - val_accuracy: 0.8247\n",
      "epoch 309\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.3479 - accuracy: 0.8763 - val_loss: 0.6017 - val_accuracy: 0.8182\n",
      "epoch 310\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.3469 - accuracy: 0.8777 - val_loss: 0.5639 - val_accuracy: 0.8377\n",
      "epoch 311\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.3404 - accuracy: 0.8792 - val_loss: 0.5956 - val_accuracy: 0.8019\n",
      "epoch 312\n",
      "44/44 [==============================] - 2s 42ms/step - loss: 0.3315 - accuracy: 0.8810 - val_loss: 0.5619 - val_accuracy: 0.8279\n",
      "epoch 313\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.3285 - accuracy: 0.8802 - val_loss: 0.5596 - val_accuracy: 0.8149\n",
      "epoch 314\n",
      "44/44 [==============================] - 2s 56ms/step - loss: 0.3350 - accuracy: 0.8774 - val_loss: 0.5642 - val_accuracy: 0.8214\n",
      "epoch 315\n",
      "44/44 [==============================] - 2s 50ms/step - loss: 0.3407 - accuracy: 0.8745 - val_loss: 0.5572 - val_accuracy: 0.8214\n",
      "epoch 316\n",
      "44/44 [==============================] - 2s 42ms/step - loss: 0.3332 - accuracy: 0.8766 - val_loss: 0.5606 - val_accuracy: 0.8344\n",
      "epoch 317\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.3296 - accuracy: 0.8831 - val_loss: 0.5644 - val_accuracy: 0.8279\n",
      "epoch 318\n",
      "44/44 [==============================] - 2s 43ms/step - loss: 0.3201 - accuracy: 0.8875 - val_loss: 0.5620 - val_accuracy: 0.8344\n",
      "epoch 319\n",
      "44/44 [==============================] - 2s 43ms/step - loss: 0.3313 - accuracy: 0.8748 - val_loss: 0.5482 - val_accuracy: 0.8312\n",
      "epoch 320\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.3353 - accuracy: 0.8781 - val_loss: 0.5914 - val_accuracy: 0.8117\n",
      "epoch 321\n",
      "44/44 [==============================] - 2s 48ms/step - loss: 0.3350 - accuracy: 0.8777 - val_loss: 0.5744 - val_accuracy: 0.8214\n",
      "epoch 322\n",
      "44/44 [==============================] - 2s 54ms/step - loss: 0.3257 - accuracy: 0.8821 - val_loss: 0.5545 - val_accuracy: 0.8409\n",
      "epoch 323\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.3308 - accuracy: 0.8846 - val_loss: 0.5976 - val_accuracy: 0.8084\n",
      "epoch 324\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.3453 - accuracy: 0.8723 - val_loss: 0.6085 - val_accuracy: 0.8117\n",
      "epoch 325\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.3371 - accuracy: 0.8821 - val_loss: 0.5572 - val_accuracy: 0.8247\n",
      "epoch 326\n",
      "44/44 [==============================] - 2s 44ms/step - loss: 0.3228 - accuracy: 0.8810 - val_loss: 0.5700 - val_accuracy: 0.8279\n",
      "epoch 327\n",
      "44/44 [==============================] - 2s 45ms/step - loss: 0.3225 - accuracy: 0.8860 - val_loss: 0.5615 - val_accuracy: 0.8214\n",
      "epoch 328\n",
      "44/44 [==============================] - 2s 43ms/step - loss: 0.3179 - accuracy: 0.8878 - val_loss: 0.5668 - val_accuracy: 0.8312\n",
      "epoch 329\n",
      "44/44 [==============================] - 2s 42ms/step - loss: 0.3123 - accuracy: 0.8839 - val_loss: 0.5725 - val_accuracy: 0.8344\n",
      "epoch 330\n",
      "44/44 [==============================] - 2s 43ms/step - loss: 0.3303 - accuracy: 0.8770 - val_loss: 0.5769 - val_accuracy: 0.8182\n",
      "epoch 331\n",
      "44/44 [==============================] - 2s 55ms/step - loss: 0.3155 - accuracy: 0.8860 - val_loss: 0.6049 - val_accuracy: 0.8214\n",
      "epoch 332\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.3257 - accuracy: 0.8849 - val_loss: 0.5476 - val_accuracy: 0.8344\n",
      "epoch 333\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.3336 - accuracy: 0.8755 - val_loss: 0.5654 - val_accuracy: 0.8279\n",
      "epoch 334\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.3402 - accuracy: 0.8799 - val_loss: 0.5527 - val_accuracy: 0.8247\n",
      "epoch 335\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.3192 - accuracy: 0.8842 - val_loss: 0.5719 - val_accuracy: 0.8214\n",
      "epoch 336\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.3236 - accuracy: 0.8813 - val_loss: 0.5667 - val_accuracy: 0.8312\n",
      "epoch 337\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.3363 - accuracy: 0.8792 - val_loss: 0.5584 - val_accuracy: 0.8312\n",
      "epoch 338\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.3247 - accuracy: 0.8846 - val_loss: 0.5481 - val_accuracy: 0.8344\n",
      "epoch 339\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.3216 - accuracy: 0.8835 - val_loss: 0.5817 - val_accuracy: 0.8214\n",
      "epoch 340\n",
      "44/44 [==============================] - 2s 45ms/step - loss: 0.3223 - accuracy: 0.8839 - val_loss: 0.5968 - val_accuracy: 0.8117\n",
      "epoch 341\n",
      "44/44 [==============================] - 2s 49ms/step - loss: 0.3272 - accuracy: 0.8846 - val_loss: 0.5905 - val_accuracy: 0.8117\n",
      "epoch 342\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.3273 - accuracy: 0.8886 - val_loss: 0.5717 - val_accuracy: 0.8247\n",
      "epoch 343\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.3182 - accuracy: 0.8849 - val_loss: 0.5434 - val_accuracy: 0.8312\n",
      "epoch 344\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.3200 - accuracy: 0.8886 - val_loss: 0.5496 - val_accuracy: 0.8214\n",
      "epoch 345\n",
      "44/44 [==============================] - 2s 44ms/step - loss: 0.3152 - accuracy: 0.8904 - val_loss: 0.5353 - val_accuracy: 0.8409\n",
      "epoch 346\n",
      "44/44 [==============================] - 2s 53ms/step - loss: 0.3153 - accuracy: 0.8828 - val_loss: 0.5640 - val_accuracy: 0.8247\n",
      "epoch 347\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.3208 - accuracy: 0.8846 - val_loss: 0.5531 - val_accuracy: 0.8214\n",
      "epoch 348\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.3208 - accuracy: 0.8784 - val_loss: 0.5450 - val_accuracy: 0.8312\n",
      "epoch 349\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.3130 - accuracy: 0.8904 - val_loss: 0.5758 - val_accuracy: 0.8182\n",
      "epoch 350\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.3135 - accuracy: 0.8857 - val_loss: 0.6292 - val_accuracy: 0.8117\n",
      "epoch 351\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.3337 - accuracy: 0.8774 - val_loss: 0.5636 - val_accuracy: 0.8344\n",
      "epoch 352\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.3244 - accuracy: 0.8755 - val_loss: 0.5679 - val_accuracy: 0.8344\n",
      "epoch 353\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.3150 - accuracy: 0.8868 - val_loss: 0.5599 - val_accuracy: 0.8214\n",
      "epoch 354\n",
      "44/44 [==============================] - 2s 44ms/step - loss: 0.3138 - accuracy: 0.8889 - val_loss: 0.5632 - val_accuracy: 0.8279\n",
      "epoch 355\n",
      "44/44 [==============================] - 2s 43ms/step - loss: 0.3061 - accuracy: 0.8889 - val_loss: 0.5605 - val_accuracy: 0.8279\n",
      "epoch 356\n",
      "44/44 [==============================] - 2s 51ms/step - loss: 0.3105 - accuracy: 0.8871 - val_loss: 0.5594 - val_accuracy: 0.8279\n",
      "epoch 357\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.3174 - accuracy: 0.8860 - val_loss: 0.5494 - val_accuracy: 0.8377\n",
      "epoch 358\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.2996 - accuracy: 0.8983 - val_loss: 0.5576 - val_accuracy: 0.8344\n",
      "epoch 359\n",
      "44/44 [==============================] - 2s 49ms/step - loss: 0.3089 - accuracy: 0.8878 - val_loss: 0.5471 - val_accuracy: 0.8279\n",
      "epoch 360\n",
      "44/44 [==============================] - 2s 50ms/step - loss: 0.3111 - accuracy: 0.8882 - val_loss: 0.5849 - val_accuracy: 0.8312\n",
      "epoch 361\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.3088 - accuracy: 0.8886 - val_loss: 0.5452 - val_accuracy: 0.8247\n",
      "epoch 362\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.3120 - accuracy: 0.8849 - val_loss: 0.5596 - val_accuracy: 0.8312\n",
      "epoch 363\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.3179 - accuracy: 0.8824 - val_loss: 0.5293 - val_accuracy: 0.8377\n",
      "epoch 364\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.3027 - accuracy: 0.8936 - val_loss: 0.5800 - val_accuracy: 0.8117\n",
      "epoch 365\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.3041 - accuracy: 0.8864 - val_loss: 0.5662 - val_accuracy: 0.8247\n",
      "epoch 366\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.3095 - accuracy: 0.8864 - val_loss: 0.6374 - val_accuracy: 0.8149\n",
      "epoch 367\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.3170 - accuracy: 0.8849 - val_loss: 0.5544 - val_accuracy: 0.8279\n",
      "epoch 368\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.3094 - accuracy: 0.8875 - val_loss: 0.5487 - val_accuracy: 0.8377\n",
      "epoch 369\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.3044 - accuracy: 0.8886 - val_loss: 0.5612 - val_accuracy: 0.8247\n",
      "epoch 370\n",
      "44/44 [==============================] - 2s 49ms/step - loss: 0.3291 - accuracy: 0.8806 - val_loss: 0.5993 - val_accuracy: 0.8052\n",
      "epoch 371\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.3063 - accuracy: 0.8882 - val_loss: 0.5547 - val_accuracy: 0.8279\n",
      "epoch 372\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.3045 - accuracy: 0.8893 - val_loss: 0.5568 - val_accuracy: 0.8214\n",
      "epoch 373\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.3029 - accuracy: 0.8904 - val_loss: 0.5956 - val_accuracy: 0.8117\n",
      "epoch 374\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.3044 - accuracy: 0.8893 - val_loss: 0.5422 - val_accuracy: 0.8312\n",
      "epoch 375\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2973 - accuracy: 0.8958 - val_loss: 0.5743 - val_accuracy: 0.8279\n",
      "epoch 376\n",
      "44/44 [==============================] - 2s 53ms/step - loss: 0.3111 - accuracy: 0.8900 - val_loss: 0.5450 - val_accuracy: 0.8344\n",
      "epoch 377\n",
      "44/44 [==============================] - 2s 46ms/step - loss: 0.3012 - accuracy: 0.8911 - val_loss: 0.5434 - val_accuracy: 0.8344\n",
      "epoch 378\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.3134 - accuracy: 0.8889 - val_loss: 0.5509 - val_accuracy: 0.8506\n",
      "epoch 379\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.3004 - accuracy: 0.8911 - val_loss: 0.5775 - val_accuracy: 0.8279\n",
      "epoch 380\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.3057 - accuracy: 0.8893 - val_loss: 0.6053 - val_accuracy: 0.8214\n",
      "epoch 381\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.3113 - accuracy: 0.8915 - val_loss: 0.5735 - val_accuracy: 0.8312\n",
      "epoch 382\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.3015 - accuracy: 0.8868 - val_loss: 0.5401 - val_accuracy: 0.8409\n",
      "epoch 383\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.3048 - accuracy: 0.8900 - val_loss: 0.5508 - val_accuracy: 0.8279\n",
      "epoch 384\n",
      "44/44 [==============================] - 2s 47ms/step - loss: 0.3142 - accuracy: 0.8886 - val_loss: 0.5417 - val_accuracy: 0.8247\n",
      "epoch 385\n",
      "44/44 [==============================] - 2s 52ms/step - loss: 0.3063 - accuracy: 0.8900 - val_loss: 0.5378 - val_accuracy: 0.8312\n",
      "epoch 386\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.2992 - accuracy: 0.8900 - val_loss: 0.5649 - val_accuracy: 0.8279\n",
      "epoch 387\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.2932 - accuracy: 0.8951 - val_loss: 0.5597 - val_accuracy: 0.8409\n",
      "epoch 388\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.3009 - accuracy: 0.8922 - val_loss: 0.5535 - val_accuracy: 0.8312\n",
      "epoch 389\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.3052 - accuracy: 0.8868 - val_loss: 0.5407 - val_accuracy: 0.8279\n",
      "epoch 390\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.2868 - accuracy: 0.8929 - val_loss: 0.5535 - val_accuracy: 0.8247\n",
      "epoch 391\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.3010 - accuracy: 0.8958 - val_loss: 0.5500 - val_accuracy: 0.8182\n",
      "epoch 392\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.2891 - accuracy: 0.9023 - val_loss: 0.5490 - val_accuracy: 0.8312\n",
      "epoch 393\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.2909 - accuracy: 0.8925 - val_loss: 0.5369 - val_accuracy: 0.8279\n",
      "epoch 394\n",
      "44/44 [==============================] - 2s 50ms/step - loss: 0.2849 - accuracy: 0.8976 - val_loss: 0.5429 - val_accuracy: 0.8247\n",
      "epoch 395\n",
      "44/44 [==============================] - 2s 50ms/step - loss: 0.2812 - accuracy: 0.9001 - val_loss: 0.5500 - val_accuracy: 0.8279\n",
      "epoch 396\n",
      "44/44 [==============================] - 2s 42ms/step - loss: 0.2889 - accuracy: 0.8973 - val_loss: 0.5467 - val_accuracy: 0.8312\n",
      "epoch 397\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.2957 - accuracy: 0.8929 - val_loss: 0.5676 - val_accuracy: 0.8182\n",
      "epoch 398\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.2840 - accuracy: 0.9005 - val_loss: 0.5490 - val_accuracy: 0.8214\n",
      "epoch 399\n",
      "44/44 [==============================] - 2s 50ms/step - loss: 0.2855 - accuracy: 0.9005 - val_loss: 0.5658 - val_accuracy: 0.8279\n",
      "epoch 400\n",
      "44/44 [==============================] - 2s 51ms/step - loss: 0.2876 - accuracy: 0.8998 - val_loss: 0.5484 - val_accuracy: 0.8344\n",
      "epoch 401\n",
      "44/44 [==============================] - 2s 46ms/step - loss: 0.2844 - accuracy: 0.9009 - val_loss: 0.5646 - val_accuracy: 0.8344\n",
      "epoch 402\n",
      "44/44 [==============================] - 2s 47ms/step - loss: 0.2907 - accuracy: 0.8958 - val_loss: 0.5488 - val_accuracy: 0.8279\n",
      "epoch 403\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.2857 - accuracy: 0.8973 - val_loss: 0.5467 - val_accuracy: 0.8409\n",
      "epoch 404\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2913 - accuracy: 0.8969 - val_loss: 0.5461 - val_accuracy: 0.8377\n",
      "epoch 405\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.2858 - accuracy: 0.9001 - val_loss: 0.5456 - val_accuracy: 0.8312\n",
      "epoch 406\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.2847 - accuracy: 0.8998 - val_loss: 0.5469 - val_accuracy: 0.8442\n",
      "epoch 407\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.2908 - accuracy: 0.8969 - val_loss: 0.5487 - val_accuracy: 0.8312\n",
      "epoch 408\n",
      "44/44 [==============================] - 2s 46ms/step - loss: 0.2956 - accuracy: 0.8907 - val_loss: 0.5889 - val_accuracy: 0.7922\n",
      "epoch 409\n",
      "44/44 [==============================] - 2s 46ms/step - loss: 0.2880 - accuracy: 0.8980 - val_loss: 0.5416 - val_accuracy: 0.8247\n",
      "epoch 410\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.2830 - accuracy: 0.8958 - val_loss: 0.5444 - val_accuracy: 0.8312\n",
      "epoch 411\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.2712 - accuracy: 0.9056 - val_loss: 0.5665 - val_accuracy: 0.8279\n",
      "epoch 412\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2824 - accuracy: 0.9009 - val_loss: 0.5625 - val_accuracy: 0.8182\n",
      "epoch 413\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.3109 - accuracy: 0.8893 - val_loss: 0.5655 - val_accuracy: 0.8409\n",
      "epoch 414\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.2896 - accuracy: 0.8944 - val_loss: 0.5364 - val_accuracy: 0.8214\n",
      "epoch 415\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.2756 - accuracy: 0.8976 - val_loss: 0.5577 - val_accuracy: 0.8312\n",
      "epoch 416\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.2701 - accuracy: 0.9110 - val_loss: 0.5415 - val_accuracy: 0.8409\n",
      "epoch 417\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2769 - accuracy: 0.9001 - val_loss: 0.5579 - val_accuracy: 0.8214\n",
      "epoch 418\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.2868 - accuracy: 0.8991 - val_loss: 0.5404 - val_accuracy: 0.8344\n",
      "epoch 419\n",
      "44/44 [==============================] - 2s 55ms/step - loss: 0.2798 - accuracy: 0.9023 - val_loss: 0.5597 - val_accuracy: 0.8312\n",
      "epoch 420\n",
      "44/44 [==============================] - 2s 46ms/step - loss: 0.2903 - accuracy: 0.8987 - val_loss: 0.6142 - val_accuracy: 0.8149\n",
      "epoch 421\n",
      "44/44 [==============================] - 2s 43ms/step - loss: 0.3057 - accuracy: 0.8933 - val_loss: 0.5826 - val_accuracy: 0.8084\n",
      "epoch 422\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.2742 - accuracy: 0.8976 - val_loss: 0.5463 - val_accuracy: 0.8279\n",
      "epoch 423\n",
      "44/44 [==============================] - 2s 44ms/step - loss: 0.2801 - accuracy: 0.8998 - val_loss: 0.5468 - val_accuracy: 0.8442\n",
      "epoch 424\n",
      "44/44 [==============================] - 2s 47ms/step - loss: 0.2730 - accuracy: 0.9048 - val_loss: 0.5519 - val_accuracy: 0.8409\n",
      "epoch 425\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.2714 - accuracy: 0.9096 - val_loss: 0.5656 - val_accuracy: 0.8182\n",
      "epoch 426\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.2938 - accuracy: 0.8893 - val_loss: 0.5950 - val_accuracy: 0.8247\n",
      "epoch 427\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.2808 - accuracy: 0.9030 - val_loss: 0.5564 - val_accuracy: 0.8312\n",
      "epoch 428\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.2736 - accuracy: 0.8998 - val_loss: 0.5543 - val_accuracy: 0.8344\n",
      "epoch 429\n",
      "44/44 [==============================] - 2s 42ms/step - loss: 0.2730 - accuracy: 0.9063 - val_loss: 0.5409 - val_accuracy: 0.8409\n",
      "epoch 430\n",
      "44/44 [==============================] - 2s 50ms/step - loss: 0.2753 - accuracy: 0.9081 - val_loss: 0.5466 - val_accuracy: 0.8377\n",
      "epoch 431\n",
      "44/44 [==============================] - 2s 43ms/step - loss: 0.2814 - accuracy: 0.8983 - val_loss: 0.5870 - val_accuracy: 0.8182\n",
      "epoch 432\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.2890 - accuracy: 0.8965 - val_loss: 0.5309 - val_accuracy: 0.8539\n",
      "epoch 433\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.2843 - accuracy: 0.8936 - val_loss: 0.5430 - val_accuracy: 0.8409\n",
      "epoch 434\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.2661 - accuracy: 0.9067 - val_loss: 0.5479 - val_accuracy: 0.8344\n",
      "epoch 435\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.2783 - accuracy: 0.8976 - val_loss: 0.5719 - val_accuracy: 0.8279\n",
      "epoch 436\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.2880 - accuracy: 0.8962 - val_loss: 0.5409 - val_accuracy: 0.8539\n",
      "epoch 437\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.2640 - accuracy: 0.9085 - val_loss: 0.5504 - val_accuracy: 0.8344\n",
      "epoch 438\n",
      "44/44 [==============================] - 2s 49ms/step - loss: 0.2864 - accuracy: 0.8951 - val_loss: 0.5793 - val_accuracy: 0.8214\n",
      "epoch 439\n",
      "44/44 [==============================] - 2s 49ms/step - loss: 0.3053 - accuracy: 0.8875 - val_loss: 0.5725 - val_accuracy: 0.8052\n",
      "epoch 440\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.2905 - accuracy: 0.8944 - val_loss: 0.5385 - val_accuracy: 0.8409\n",
      "epoch 441\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.2794 - accuracy: 0.9023 - val_loss: 0.5512 - val_accuracy: 0.8279\n",
      "epoch 442\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.2810 - accuracy: 0.8976 - val_loss: 0.5416 - val_accuracy: 0.8474\n",
      "epoch 443\n",
      "44/44 [==============================] - 2s 43ms/step - loss: 0.2603 - accuracy: 0.9103 - val_loss: 0.5488 - val_accuracy: 0.8279\n",
      "epoch 444\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.2767 - accuracy: 0.9027 - val_loss: 0.5849 - val_accuracy: 0.8279\n",
      "epoch 445\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.2773 - accuracy: 0.9030 - val_loss: 0.5725 - val_accuracy: 0.8312\n",
      "epoch 446\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.2734 - accuracy: 0.9056 - val_loss: 0.5551 - val_accuracy: 0.8377\n",
      "epoch 447\n",
      "44/44 [==============================] - 2s 42ms/step - loss: 0.2748 - accuracy: 0.9023 - val_loss: 0.5335 - val_accuracy: 0.8377\n",
      "epoch 448\n",
      "44/44 [==============================] - 2s 50ms/step - loss: 0.2576 - accuracy: 0.9088 - val_loss: 0.5338 - val_accuracy: 0.8377\n",
      "epoch 449\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.2677 - accuracy: 0.9085 - val_loss: 0.5452 - val_accuracy: 0.8247\n",
      "epoch 450\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.2804 - accuracy: 0.9030 - val_loss: 0.5436 - val_accuracy: 0.8409\n",
      "epoch 451\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2557 - accuracy: 0.9157 - val_loss: 0.5337 - val_accuracy: 0.8474\n",
      "epoch 452\n",
      "44/44 [==============================] - 2s 42ms/step - loss: 0.2614 - accuracy: 0.9067 - val_loss: 0.5417 - val_accuracy: 0.8312\n",
      "epoch 453\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.2624 - accuracy: 0.9059 - val_loss: 0.5627 - val_accuracy: 0.8344\n",
      "epoch 454\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2689 - accuracy: 0.9063 - val_loss: 0.5583 - val_accuracy: 0.8377\n",
      "epoch 455\n",
      "44/44 [==============================] - 2s 47ms/step - loss: 0.2649 - accuracy: 0.9067 - val_loss: 0.5511 - val_accuracy: 0.8214\n",
      "epoch 456\n",
      "44/44 [==============================] - 2s 51ms/step - loss: 0.2575 - accuracy: 0.9099 - val_loss: 0.5537 - val_accuracy: 0.8539\n",
      "epoch 457\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.2696 - accuracy: 0.9045 - val_loss: 0.5510 - val_accuracy: 0.8539\n",
      "epoch 458\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.2574 - accuracy: 0.9092 - val_loss: 0.5450 - val_accuracy: 0.8539\n",
      "epoch 459\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.2680 - accuracy: 0.9030 - val_loss: 0.5704 - val_accuracy: 0.8279\n",
      "epoch 460\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2726 - accuracy: 0.9038 - val_loss: 0.5605 - val_accuracy: 0.8312\n",
      "epoch 461\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.2700 - accuracy: 0.9059 - val_loss: 0.5453 - val_accuracy: 0.8442\n",
      "epoch 462\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2661 - accuracy: 0.9027 - val_loss: 0.5605 - val_accuracy: 0.8442\n",
      "epoch 463\n",
      "44/44 [==============================] - 2s 46ms/step - loss: 0.2662 - accuracy: 0.9063 - val_loss: 0.5554 - val_accuracy: 0.8442\n",
      "epoch 464\n",
      "44/44 [==============================] - 2s 52ms/step - loss: 0.2639 - accuracy: 0.9045 - val_loss: 0.5467 - val_accuracy: 0.8409\n",
      "epoch 465\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.2705 - accuracy: 0.9009 - val_loss: 0.5839 - val_accuracy: 0.8279\n",
      "epoch 466\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.2684 - accuracy: 0.9059 - val_loss: 0.5739 - val_accuracy: 0.8279\n",
      "epoch 467\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.2730 - accuracy: 0.9056 - val_loss: 0.5464 - val_accuracy: 0.8474\n",
      "epoch 468\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.2686 - accuracy: 0.9088 - val_loss: 0.5809 - val_accuracy: 0.8214\n",
      "epoch 469\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.2645 - accuracy: 0.9005 - val_loss: 0.5467 - val_accuracy: 0.8474\n",
      "epoch 470\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.2579 - accuracy: 0.9103 - val_loss: 0.5654 - val_accuracy: 0.8604\n",
      "epoch 471\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2611 - accuracy: 0.9063 - val_loss: 0.5482 - val_accuracy: 0.8312\n",
      "epoch 472\n",
      "44/44 [==============================] - 2s 47ms/step - loss: 0.2575 - accuracy: 0.9106 - val_loss: 0.6241 - val_accuracy: 0.8117\n",
      "epoch 473\n",
      "44/44 [==============================] - 2s 45ms/step - loss: 0.2874 - accuracy: 0.8987 - val_loss: 0.5501 - val_accuracy: 0.8409\n",
      "epoch 474\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.2628 - accuracy: 0.9052 - val_loss: 0.5878 - val_accuracy: 0.8312\n",
      "epoch 475\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2534 - accuracy: 0.9077 - val_loss: 0.5383 - val_accuracy: 0.8442\n",
      "epoch 476\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2668 - accuracy: 0.9052 - val_loss: 0.5432 - val_accuracy: 0.8571\n",
      "epoch 477\n",
      "44/44 [==============================] - 2s 47ms/step - loss: 0.2521 - accuracy: 0.9124 - val_loss: 0.5451 - val_accuracy: 0.8247\n",
      "epoch 478\n",
      "44/44 [==============================] - 2s 46ms/step - loss: 0.2550 - accuracy: 0.9067 - val_loss: 0.5602 - val_accuracy: 0.8182\n",
      "epoch 479\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2554 - accuracy: 0.9103 - val_loss: 0.5369 - val_accuracy: 0.8506\n",
      "epoch 480\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2528 - accuracy: 0.9088 - val_loss: 0.5308 - val_accuracy: 0.8474\n",
      "epoch 481\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2530 - accuracy: 0.9106 - val_loss: 0.5796 - val_accuracy: 0.8214\n",
      "epoch 482\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2742 - accuracy: 0.8976 - val_loss: 0.5737 - val_accuracy: 0.8409\n",
      "epoch 483\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2518 - accuracy: 0.9099 - val_loss: 0.5396 - val_accuracy: 0.8571\n",
      "epoch 484\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2611 - accuracy: 0.9077 - val_loss: 0.5406 - val_accuracy: 0.8279\n",
      "epoch 485\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2630 - accuracy: 0.9048 - val_loss: 0.5629 - val_accuracy: 0.8279\n",
      "epoch 486\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2516 - accuracy: 0.9153 - val_loss: 0.5516 - val_accuracy: 0.8377\n",
      "epoch 487\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2530 - accuracy: 0.9106 - val_loss: 0.5337 - val_accuracy: 0.8474\n",
      "epoch 488\n",
      "44/44 [==============================] - 2s 48ms/step - loss: 0.2510 - accuracy: 0.9132 - val_loss: 0.5520 - val_accuracy: 0.8409\n",
      "epoch 489\n",
      "44/44 [==============================] - 2s 44ms/step - loss: 0.2521 - accuracy: 0.9088 - val_loss: 0.5503 - val_accuracy: 0.8474\n",
      "epoch 490\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2420 - accuracy: 0.9146 - val_loss: 0.5688 - val_accuracy: 0.8442\n",
      "epoch 491\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2480 - accuracy: 0.9124 - val_loss: 0.5635 - val_accuracy: 0.8247\n",
      "epoch 492\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2453 - accuracy: 0.9096 - val_loss: 0.5379 - val_accuracy: 0.8506\n",
      "epoch 493\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2635 - accuracy: 0.9048 - val_loss: 0.5735 - val_accuracy: 0.8214\n",
      "epoch 494\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2570 - accuracy: 0.9099 - val_loss: 0.5446 - val_accuracy: 0.8539\n",
      "epoch 495\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2446 - accuracy: 0.9150 - val_loss: 0.5565 - val_accuracy: 0.8377\n",
      "epoch 496\n",
      "44/44 [==============================] - 2s 45ms/step - loss: 0.2538 - accuracy: 0.9110 - val_loss: 0.5476 - val_accuracy: 0.8409\n",
      "epoch 497\n",
      "44/44 [==============================] - 2s 54ms/step - loss: 0.2484 - accuracy: 0.9153 - val_loss: 0.5381 - val_accuracy: 0.8409\n",
      "epoch 498\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.2438 - accuracy: 0.9135 - val_loss: 0.5358 - val_accuracy: 0.8442\n",
      "epoch 499\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.2481 - accuracy: 0.9124 - val_loss: 0.5451 - val_accuracy: 0.8474\n",
      "epoch 500\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2514 - accuracy: 0.9081 - val_loss: 0.5584 - val_accuracy: 0.8474\n",
      "epoch 501\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2369 - accuracy: 0.9153 - val_loss: 0.5339 - val_accuracy: 0.8409\n",
      "epoch 502\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.2417 - accuracy: 0.9168 - val_loss: 0.6054 - val_accuracy: 0.8182\n",
      "epoch 503\n",
      "44/44 [==============================] - 2s 45ms/step - loss: 0.2743 - accuracy: 0.8965 - val_loss: 0.5432 - val_accuracy: 0.8474\n",
      "epoch 504\n",
      "44/44 [==============================] - 2s 46ms/step - loss: 0.2501 - accuracy: 0.9103 - val_loss: 0.5791 - val_accuracy: 0.8442\n",
      "epoch 505\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.2430 - accuracy: 0.9186 - val_loss: 0.5460 - val_accuracy: 0.8474\n",
      "epoch 506\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2423 - accuracy: 0.9150 - val_loss: 0.5412 - val_accuracy: 0.8409\n",
      "epoch 507\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2577 - accuracy: 0.9103 - val_loss: 0.5791 - val_accuracy: 0.8344\n",
      "epoch 508\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.2660 - accuracy: 0.9023 - val_loss: 0.5715 - val_accuracy: 0.8377\n",
      "epoch 509\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2540 - accuracy: 0.9074 - val_loss: 0.5847 - val_accuracy: 0.8539\n",
      "epoch 510\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2531 - accuracy: 0.9110 - val_loss: 0.5470 - val_accuracy: 0.8539\n",
      "epoch 511\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2500 - accuracy: 0.9106 - val_loss: 0.5657 - val_accuracy: 0.8279\n",
      "epoch 512\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2405 - accuracy: 0.9121 - val_loss: 0.5587 - val_accuracy: 0.8377\n",
      "epoch 513\n",
      "44/44 [==============================] - 2s 42ms/step - loss: 0.2447 - accuracy: 0.9114 - val_loss: 0.5517 - val_accuracy: 0.8442\n",
      "epoch 514\n",
      "44/44 [==============================] - 2s 49ms/step - loss: 0.2461 - accuracy: 0.9121 - val_loss: 0.5842 - val_accuracy: 0.8312\n",
      "epoch 515\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.2426 - accuracy: 0.9132 - val_loss: 0.5545 - val_accuracy: 0.8344\n",
      "epoch 516\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2431 - accuracy: 0.9124 - val_loss: 0.5389 - val_accuracy: 0.8312\n",
      "epoch 517\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2386 - accuracy: 0.9161 - val_loss: 0.6010 - val_accuracy: 0.8377\n",
      "epoch 518\n",
      "44/44 [==============================] - 2s 42ms/step - loss: 0.2366 - accuracy: 0.9168 - val_loss: 0.5580 - val_accuracy: 0.8377\n",
      "epoch 519\n",
      "44/44 [==============================] - 2s 48ms/step - loss: 0.2582 - accuracy: 0.9034 - val_loss: 0.5619 - val_accuracy: 0.8442\n",
      "epoch 520\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.2520 - accuracy: 0.9070 - val_loss: 0.5599 - val_accuracy: 0.8409\n",
      "epoch 521\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.2542 - accuracy: 0.9106 - val_loss: 0.5572 - val_accuracy: 0.8442\n",
      "epoch 522\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.2479 - accuracy: 0.9074 - val_loss: 0.5727 - val_accuracy: 0.8474\n",
      "epoch 523\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.2421 - accuracy: 0.9106 - val_loss: 0.5824 - val_accuracy: 0.8149\n",
      "epoch 524\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.2505 - accuracy: 0.9088 - val_loss: 0.6719 - val_accuracy: 0.7987\n",
      "epoch 525\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.2639 - accuracy: 0.9045 - val_loss: 0.5639 - val_accuracy: 0.8442\n",
      "epoch 526\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.2379 - accuracy: 0.9168 - val_loss: 0.5576 - val_accuracy: 0.8247\n",
      "epoch 527\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.2276 - accuracy: 0.9208 - val_loss: 0.5546 - val_accuracy: 0.8312\n",
      "epoch 528\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.2461 - accuracy: 0.9106 - val_loss: 0.5509 - val_accuracy: 0.8474\n",
      "epoch 529\n",
      "44/44 [==============================] - 2s 45ms/step - loss: 0.2346 - accuracy: 0.9190 - val_loss: 0.5404 - val_accuracy: 0.8312\n",
      "epoch 530\n",
      "44/44 [==============================] - 2s 42ms/step - loss: 0.2340 - accuracy: 0.9182 - val_loss: 0.5465 - val_accuracy: 0.8442\n",
      "epoch 531\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.2429 - accuracy: 0.9132 - val_loss: 0.5470 - val_accuracy: 0.8506\n",
      "epoch 532\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2304 - accuracy: 0.9186 - val_loss: 0.5600 - val_accuracy: 0.8474\n",
      "epoch 533\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.2420 - accuracy: 0.9121 - val_loss: 0.5587 - val_accuracy: 0.8442\n",
      "epoch 534\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2366 - accuracy: 0.9146 - val_loss: 0.5845 - val_accuracy: 0.8214\n",
      "epoch 535\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2308 - accuracy: 0.9153 - val_loss: 0.5439 - val_accuracy: 0.8442\n",
      "epoch 536\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.2285 - accuracy: 0.9190 - val_loss: 0.5538 - val_accuracy: 0.8442\n",
      "epoch 537\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.2387 - accuracy: 0.9146 - val_loss: 0.5698 - val_accuracy: 0.8539\n",
      "epoch 538\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.2342 - accuracy: 0.9124 - val_loss: 0.5818 - val_accuracy: 0.8409\n",
      "epoch 539\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.2414 - accuracy: 0.9171 - val_loss: 0.5510 - val_accuracy: 0.8474\n",
      "epoch 540\n",
      "44/44 [==============================] - 2s 46ms/step - loss: 0.2447 - accuracy: 0.9106 - val_loss: 0.6229 - val_accuracy: 0.8117\n",
      "epoch 541\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.2556 - accuracy: 0.9052 - val_loss: 0.5523 - val_accuracy: 0.8604\n",
      "epoch 542\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2429 - accuracy: 0.9114 - val_loss: 0.5426 - val_accuracy: 0.8409\n",
      "epoch 543\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2272 - accuracy: 0.9186 - val_loss: 0.5514 - val_accuracy: 0.8474\n",
      "epoch 544\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.2297 - accuracy: 0.9161 - val_loss: 0.5641 - val_accuracy: 0.8474\n",
      "epoch 545\n",
      "44/44 [==============================] - 2s 51ms/step - loss: 0.2292 - accuracy: 0.9222 - val_loss: 0.5791 - val_accuracy: 0.8279\n",
      "epoch 546\n",
      "44/44 [==============================] - 2s 50ms/step - loss: 0.2388 - accuracy: 0.9117 - val_loss: 0.5702 - val_accuracy: 0.8409\n",
      "epoch 547\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.2260 - accuracy: 0.9182 - val_loss: 0.5525 - val_accuracy: 0.8409\n",
      "epoch 548\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.2212 - accuracy: 0.9262 - val_loss: 0.5517 - val_accuracy: 0.8409\n",
      "epoch 549\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.2347 - accuracy: 0.9153 - val_loss: 0.5553 - val_accuracy: 0.8474\n",
      "epoch 550\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.2279 - accuracy: 0.9244 - val_loss: 0.5785 - val_accuracy: 0.8442\n",
      "epoch 551\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2476 - accuracy: 0.9096 - val_loss: 0.5465 - val_accuracy: 0.8312\n",
      "epoch 552\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2269 - accuracy: 0.9186 - val_loss: 0.5938 - val_accuracy: 0.8377\n",
      "epoch 553\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.2350 - accuracy: 0.9150 - val_loss: 0.5453 - val_accuracy: 0.8539\n",
      "epoch 554\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.2320 - accuracy: 0.9179 - val_loss: 0.5351 - val_accuracy: 0.8409\n",
      "epoch 555\n",
      "44/44 [==============================] - 2s 48ms/step - loss: 0.2193 - accuracy: 0.9226 - val_loss: 0.5667 - val_accuracy: 0.8474\n",
      "epoch 556\n",
      "44/44 [==============================] - 2s 47ms/step - loss: 0.2250 - accuracy: 0.9186 - val_loss: 0.5631 - val_accuracy: 0.8442\n",
      "epoch 557\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.2169 - accuracy: 0.9240 - val_loss: 0.5645 - val_accuracy: 0.8506\n",
      "epoch 558\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.2281 - accuracy: 0.9244 - val_loss: 0.5469 - val_accuracy: 0.8344\n",
      "epoch 559\n",
      "44/44 [==============================] - 2s 42ms/step - loss: 0.2284 - accuracy: 0.9168 - val_loss: 0.5655 - val_accuracy: 0.8344\n",
      "epoch 560\n",
      "44/44 [==============================] - 2s 49ms/step - loss: 0.2288 - accuracy: 0.9219 - val_loss: 0.5482 - val_accuracy: 0.8474\n",
      "epoch 561\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2238 - accuracy: 0.9222 - val_loss: 0.5676 - val_accuracy: 0.8377\n",
      "epoch 562\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.2165 - accuracy: 0.9233 - val_loss: 0.5741 - val_accuracy: 0.8377\n",
      "epoch 563\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.2340 - accuracy: 0.9182 - val_loss: 0.5800 - val_accuracy: 0.8279\n",
      "epoch 564\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2377 - accuracy: 0.9088 - val_loss: 0.5742 - val_accuracy: 0.8377\n",
      "epoch 565\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.2332 - accuracy: 0.9182 - val_loss: 0.5627 - val_accuracy: 0.8344\n",
      "epoch 566\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2281 - accuracy: 0.9222 - val_loss: 0.5677 - val_accuracy: 0.8506\n",
      "epoch 567\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.2315 - accuracy: 0.9171 - val_loss: 0.5836 - val_accuracy: 0.8344\n",
      "epoch 568\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.2337 - accuracy: 0.9168 - val_loss: 0.5611 - val_accuracy: 0.8442\n",
      "epoch 569\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2224 - accuracy: 0.9215 - val_loss: 0.5913 - val_accuracy: 0.8344\n",
      "epoch 570\n",
      "44/44 [==============================] - 2s 50ms/step - loss: 0.2237 - accuracy: 0.9186 - val_loss: 0.5741 - val_accuracy: 0.8409\n",
      "epoch 571\n",
      "44/44 [==============================] - 2s 46ms/step - loss: 0.2325 - accuracy: 0.9179 - val_loss: 0.5748 - val_accuracy: 0.8442\n",
      "epoch 572\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.2148 - accuracy: 0.9251 - val_loss: 0.5708 - val_accuracy: 0.8344\n",
      "epoch 573\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.2234 - accuracy: 0.9255 - val_loss: 0.5722 - val_accuracy: 0.8506\n",
      "epoch 574\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.2170 - accuracy: 0.9255 - val_loss: 0.5449 - val_accuracy: 0.8409\n",
      "epoch 575\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.2219 - accuracy: 0.9208 - val_loss: 0.5578 - val_accuracy: 0.8279\n",
      "epoch 576\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2188 - accuracy: 0.9244 - val_loss: 0.5516 - val_accuracy: 0.8409\n",
      "epoch 577\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.2143 - accuracy: 0.9295 - val_loss: 0.5534 - val_accuracy: 0.8377\n",
      "epoch 578\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.2167 - accuracy: 0.9247 - val_loss: 0.5704 - val_accuracy: 0.8247\n",
      "epoch 579\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.2134 - accuracy: 0.9247 - val_loss: 0.5404 - val_accuracy: 0.8474\n",
      "epoch 580\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.2189 - accuracy: 0.9229 - val_loss: 0.5821 - val_accuracy: 0.8344\n",
      "epoch 581\n",
      "44/44 [==============================] - 2s 49ms/step - loss: 0.2212 - accuracy: 0.9219 - val_loss: 0.5644 - val_accuracy: 0.8474\n",
      "epoch 582\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.2176 - accuracy: 0.9219 - val_loss: 0.5653 - val_accuracy: 0.8409\n",
      "epoch 583\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.2149 - accuracy: 0.9240 - val_loss: 0.5501 - val_accuracy: 0.8604\n",
      "epoch 584\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2132 - accuracy: 0.9255 - val_loss: 0.5488 - val_accuracy: 0.8604\n",
      "epoch 585\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2209 - accuracy: 0.9211 - val_loss: 0.5591 - val_accuracy: 0.8377\n",
      "epoch 586\n",
      "44/44 [==============================] - 2s 43ms/step - loss: 0.2276 - accuracy: 0.9193 - val_loss: 0.5622 - val_accuracy: 0.8442\n",
      "epoch 587\n",
      "44/44 [==============================] - 2s 46ms/step - loss: 0.2319 - accuracy: 0.9233 - val_loss: 0.6436 - val_accuracy: 0.8344\n",
      "epoch 588\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.2356 - accuracy: 0.9139 - val_loss: 0.5484 - val_accuracy: 0.8669\n",
      "epoch 589\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2060 - accuracy: 0.9291 - val_loss: 0.5562 - val_accuracy: 0.8214\n",
      "epoch 590\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2076 - accuracy: 0.9276 - val_loss: 0.5766 - val_accuracy: 0.8409\n",
      "epoch 591\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2029 - accuracy: 0.9298 - val_loss: 0.5555 - val_accuracy: 0.8377\n",
      "epoch 592\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.2078 - accuracy: 0.9316 - val_loss: 0.5676 - val_accuracy: 0.8539\n",
      "epoch 593\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.2122 - accuracy: 0.9273 - val_loss: 0.5612 - val_accuracy: 0.8539\n",
      "epoch 594\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2119 - accuracy: 0.9309 - val_loss: 0.5765 - val_accuracy: 0.8344\n",
      "epoch 595\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.2128 - accuracy: 0.9222 - val_loss: 0.5429 - val_accuracy: 0.8442\n",
      "epoch 596\n",
      "44/44 [==============================] - 2s 47ms/step - loss: 0.2039 - accuracy: 0.9331 - val_loss: 0.5593 - val_accuracy: 0.8442\n",
      "epoch 597\n",
      "44/44 [==============================] - 2s 45ms/step - loss: 0.2080 - accuracy: 0.9262 - val_loss: 0.5740 - val_accuracy: 0.8377\n",
      "epoch 598\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2050 - accuracy: 0.9287 - val_loss: 0.5785 - val_accuracy: 0.8474\n",
      "epoch 599\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.2134 - accuracy: 0.9226 - val_loss: 0.5743 - val_accuracy: 0.8506\n",
      "epoch 600\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2158 - accuracy: 0.9226 - val_loss: 0.5474 - val_accuracy: 0.8636\n",
      "epoch 601\n",
      "44/44 [==============================] - 2s 49ms/step - loss: 0.2077 - accuracy: 0.9247 - val_loss: 0.5876 - val_accuracy: 0.8409\n",
      "epoch 602\n",
      "44/44 [==============================] - 2s 42ms/step - loss: 0.2117 - accuracy: 0.9284 - val_loss: 0.5468 - val_accuracy: 0.8442\n",
      "epoch 603\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.2087 - accuracy: 0.9255 - val_loss: 0.5800 - val_accuracy: 0.8604\n",
      "epoch 604\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2030 - accuracy: 0.9327 - val_loss: 0.5661 - val_accuracy: 0.8409\n",
      "epoch 605\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2078 - accuracy: 0.9273 - val_loss: 0.5535 - val_accuracy: 0.8636\n",
      "epoch 606\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2168 - accuracy: 0.9190 - val_loss: 0.5856 - val_accuracy: 0.8539\n",
      "epoch 607\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2028 - accuracy: 0.9298 - val_loss: 0.5517 - val_accuracy: 0.8506\n",
      "epoch 608\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1998 - accuracy: 0.9305 - val_loss: 0.5792 - val_accuracy: 0.8506\n",
      "epoch 609\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2272 - accuracy: 0.9146 - val_loss: 0.5675 - val_accuracy: 0.8409\n",
      "epoch 610\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2047 - accuracy: 0.9305 - val_loss: 0.5662 - val_accuracy: 0.8474\n",
      "epoch 611\n",
      "44/44 [==============================] - 2s 46ms/step - loss: 0.2063 - accuracy: 0.9273 - val_loss: 0.5553 - val_accuracy: 0.8442\n",
      "epoch 612\n",
      "44/44 [==============================] - 2s 53ms/step - loss: 0.2176 - accuracy: 0.9208 - val_loss: 0.5438 - val_accuracy: 0.8506\n",
      "epoch 613\n",
      "44/44 [==============================] - 2s 49ms/step - loss: 0.2205 - accuracy: 0.9204 - val_loss: 0.5599 - val_accuracy: 0.8474\n",
      "epoch 614\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.2033 - accuracy: 0.9291 - val_loss: 0.5631 - val_accuracy: 0.8377\n",
      "epoch 615\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.1997 - accuracy: 0.9309 - val_loss: 0.5676 - val_accuracy: 0.8474\n",
      "epoch 616\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.2192 - accuracy: 0.9255 - val_loss: 0.5801 - val_accuracy: 0.8409\n",
      "epoch 617\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.2070 - accuracy: 0.9258 - val_loss: 0.5808 - val_accuracy: 0.8506\n",
      "epoch 618\n",
      "44/44 [==============================] - 2s 53ms/step - loss: 0.2138 - accuracy: 0.9240 - val_loss: 0.5951 - val_accuracy: 0.8442\n",
      "epoch 619\n",
      "44/44 [==============================] - 2s 48ms/step - loss: 0.2203 - accuracy: 0.9208 - val_loss: 0.6103 - val_accuracy: 0.8377\n",
      "epoch 620\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.2116 - accuracy: 0.9186 - val_loss: 0.5853 - val_accuracy: 0.8474\n",
      "epoch 621\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.2171 - accuracy: 0.9251 - val_loss: 0.5783 - val_accuracy: 0.8344\n",
      "epoch 622\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.2239 - accuracy: 0.9182 - val_loss: 0.5544 - val_accuracy: 0.8409\n",
      "epoch 623\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.2180 - accuracy: 0.9146 - val_loss: 0.5916 - val_accuracy: 0.8442\n",
      "epoch 624\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.2238 - accuracy: 0.9186 - val_loss: 0.5596 - val_accuracy: 0.8539\n",
      "epoch 625\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.2030 - accuracy: 0.9269 - val_loss: 0.5676 - val_accuracy: 0.8506\n",
      "epoch 626\n",
      "44/44 [==============================] - 2s 47ms/step - loss: 0.2022 - accuracy: 0.9302 - val_loss: 0.5757 - val_accuracy: 0.8636\n",
      "epoch 627\n",
      "44/44 [==============================] - 2s 44ms/step - loss: 0.1980 - accuracy: 0.9284 - val_loss: 0.5734 - val_accuracy: 0.8442\n",
      "epoch 628\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.2030 - accuracy: 0.9280 - val_loss: 0.5720 - val_accuracy: 0.8344\n",
      "epoch 629\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.1941 - accuracy: 0.9345 - val_loss: 0.5566 - val_accuracy: 0.8506\n",
      "epoch 630\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.1987 - accuracy: 0.9298 - val_loss: 0.5980 - val_accuracy: 0.8182\n",
      "epoch 631\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.2108 - accuracy: 0.9262 - val_loss: 0.5641 - val_accuracy: 0.8539\n",
      "epoch 632\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.1969 - accuracy: 0.9352 - val_loss: 0.5716 - val_accuracy: 0.8506\n",
      "epoch 633\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.2043 - accuracy: 0.9266 - val_loss: 0.5716 - val_accuracy: 0.8474\n",
      "epoch 634\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2029 - accuracy: 0.9251 - val_loss: 0.6145 - val_accuracy: 0.8442\n",
      "epoch 635\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.2268 - accuracy: 0.9175 - val_loss: 0.6081 - val_accuracy: 0.8442\n",
      "epoch 636\n",
      "44/44 [==============================] - 2s 46ms/step - loss: 0.2128 - accuracy: 0.9269 - val_loss: 0.5860 - val_accuracy: 0.8474\n",
      "epoch 637\n",
      "44/44 [==============================] - 2s 50ms/step - loss: 0.1988 - accuracy: 0.9320 - val_loss: 0.6090 - val_accuracy: 0.8442\n",
      "epoch 638\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2061 - accuracy: 0.9255 - val_loss: 0.5844 - val_accuracy: 0.8506\n",
      "epoch 639\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.2116 - accuracy: 0.9262 - val_loss: 0.5700 - val_accuracy: 0.8539\n",
      "epoch 640\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2075 - accuracy: 0.9251 - val_loss: 0.5875 - val_accuracy: 0.8539\n",
      "epoch 641\n",
      "44/44 [==============================] - 2s 43ms/step - loss: 0.2121 - accuracy: 0.9222 - val_loss: 0.5950 - val_accuracy: 0.8506\n",
      "epoch 642\n",
      "44/44 [==============================] - 2s 51ms/step - loss: 0.1923 - accuracy: 0.9313 - val_loss: 0.5744 - val_accuracy: 0.8539\n",
      "epoch 643\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2014 - accuracy: 0.9309 - val_loss: 0.5732 - val_accuracy: 0.8604\n",
      "epoch 644\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1964 - accuracy: 0.9323 - val_loss: 0.5910 - val_accuracy: 0.8409\n",
      "epoch 645\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.2046 - accuracy: 0.9305 - val_loss: 0.5518 - val_accuracy: 0.8506\n",
      "epoch 646\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.2304 - accuracy: 0.9081 - val_loss: 0.5859 - val_accuracy: 0.8506\n",
      "epoch 647\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1998 - accuracy: 0.9309 - val_loss: 0.5786 - val_accuracy: 0.8539\n",
      "epoch 648\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1947 - accuracy: 0.9320 - val_loss: 0.5752 - val_accuracy: 0.8474\n",
      "epoch 649\n",
      "44/44 [==============================] - 2s 48ms/step - loss: 0.2082 - accuracy: 0.9262 - val_loss: 0.5760 - val_accuracy: 0.8539\n",
      "epoch 650\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1931 - accuracy: 0.9331 - val_loss: 0.5726 - val_accuracy: 0.8539\n",
      "epoch 651\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2000 - accuracy: 0.9287 - val_loss: 0.5804 - val_accuracy: 0.8571\n",
      "epoch 652\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2016 - accuracy: 0.9291 - val_loss: 0.5607 - val_accuracy: 0.8442\n",
      "epoch 653\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.1916 - accuracy: 0.9345 - val_loss: 0.5608 - val_accuracy: 0.8409\n",
      "epoch 654\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.1903 - accuracy: 0.9316 - val_loss: 0.5650 - val_accuracy: 0.8506\n",
      "epoch 655\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.1945 - accuracy: 0.9360 - val_loss: 0.5473 - val_accuracy: 0.8636\n",
      "epoch 656\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1954 - accuracy: 0.9313 - val_loss: 0.5615 - val_accuracy: 0.8571\n",
      "epoch 657\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1969 - accuracy: 0.9298 - val_loss: 0.5744 - val_accuracy: 0.8442\n",
      "epoch 658\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1977 - accuracy: 0.9291 - val_loss: 0.5593 - val_accuracy: 0.8539\n",
      "epoch 659\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.2024 - accuracy: 0.9273 - val_loss: 0.5930 - val_accuracy: 0.8474\n",
      "epoch 660\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1932 - accuracy: 0.9302 - val_loss: 0.5540 - val_accuracy: 0.8604\n",
      "epoch 661\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1855 - accuracy: 0.9363 - val_loss: 0.5697 - val_accuracy: 0.8409\n",
      "epoch 662\n",
      "44/44 [==============================] - 2s 45ms/step - loss: 0.1942 - accuracy: 0.9352 - val_loss: 0.5698 - val_accuracy: 0.8474\n",
      "epoch 663\n",
      "44/44 [==============================] - 2s 52ms/step - loss: 0.1844 - accuracy: 0.9360 - val_loss: 0.5635 - val_accuracy: 0.8539\n",
      "epoch 664\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.1892 - accuracy: 0.9356 - val_loss: 0.6237 - val_accuracy: 0.8312\n",
      "epoch 665\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.1983 - accuracy: 0.9298 - val_loss: 0.5796 - val_accuracy: 0.8377\n",
      "epoch 666\n",
      "44/44 [==============================] - 2s 45ms/step - loss: 0.1926 - accuracy: 0.9291 - val_loss: 0.5823 - val_accuracy: 0.8377\n",
      "epoch 667\n",
      "44/44 [==============================] - 2s 48ms/step - loss: 0.1924 - accuracy: 0.9316 - val_loss: 0.5696 - val_accuracy: 0.8539\n",
      "epoch 668\n",
      "44/44 [==============================] - 2s 43ms/step - loss: 0.1981 - accuracy: 0.9309 - val_loss: 0.5759 - val_accuracy: 0.8474\n",
      "epoch 669\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.1815 - accuracy: 0.9396 - val_loss: 0.5512 - val_accuracy: 0.8539\n",
      "epoch 670\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.1833 - accuracy: 0.9407 - val_loss: 0.5786 - val_accuracy: 0.8539\n",
      "epoch 671\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.1926 - accuracy: 0.9327 - val_loss: 0.5656 - val_accuracy: 0.8506\n",
      "epoch 672\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1862 - accuracy: 0.9352 - val_loss: 0.5904 - val_accuracy: 0.8539\n",
      "epoch 673\n",
      "44/44 [==============================] - 2s 43ms/step - loss: 0.1974 - accuracy: 0.9255 - val_loss: 0.5835 - val_accuracy: 0.8474\n",
      "epoch 674\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.1943 - accuracy: 0.9320 - val_loss: 0.5741 - val_accuracy: 0.8636\n",
      "epoch 675\n",
      "44/44 [==============================] - 2s 53ms/step - loss: 0.1930 - accuracy: 0.9313 - val_loss: 0.5815 - val_accuracy: 0.8604\n",
      "epoch 676\n",
      "44/44 [==============================] - 2s 46ms/step - loss: 0.1969 - accuracy: 0.9349 - val_loss: 0.6078 - val_accuracy: 0.8312\n",
      "epoch 677\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1860 - accuracy: 0.9374 - val_loss: 0.5866 - val_accuracy: 0.8506\n",
      "epoch 678\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1889 - accuracy: 0.9287 - val_loss: 0.5775 - val_accuracy: 0.8506\n",
      "epoch 679\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1877 - accuracy: 0.9360 - val_loss: 0.6042 - val_accuracy: 0.8409\n",
      "epoch 680\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.1872 - accuracy: 0.9334 - val_loss: 0.5813 - val_accuracy: 0.8409\n",
      "epoch 681\n",
      "44/44 [==============================] - 2s 46ms/step - loss: 0.1879 - accuracy: 0.9345 - val_loss: 0.5728 - val_accuracy: 0.8571\n",
      "epoch 682\n",
      "44/44 [==============================] - 2s 48ms/step - loss: 0.1835 - accuracy: 0.9370 - val_loss: 0.5837 - val_accuracy: 0.8571\n",
      "epoch 683\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1801 - accuracy: 0.9374 - val_loss: 0.5789 - val_accuracy: 0.8539\n",
      "epoch 684\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1821 - accuracy: 0.9389 - val_loss: 0.5723 - val_accuracy: 0.8506\n",
      "epoch 685\n",
      "44/44 [==============================] - 2s 45ms/step - loss: 0.1835 - accuracy: 0.9356 - val_loss: 0.5814 - val_accuracy: 0.8604\n",
      "epoch 686\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.2058 - accuracy: 0.9280 - val_loss: 0.5726 - val_accuracy: 0.8474\n",
      "epoch 687\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.2039 - accuracy: 0.9269 - val_loss: 0.5898 - val_accuracy: 0.8506\n",
      "epoch 688\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.1842 - accuracy: 0.9363 - val_loss: 0.5835 - val_accuracy: 0.8474\n",
      "epoch 689\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.1888 - accuracy: 0.9352 - val_loss: 0.5663 - val_accuracy: 0.8539\n",
      "epoch 690\n",
      "44/44 [==============================] - 2s 43ms/step - loss: 0.1798 - accuracy: 0.9414 - val_loss: 0.5850 - val_accuracy: 0.8604\n",
      "epoch 691\n",
      "44/44 [==============================] - 2s 48ms/step - loss: 0.1903 - accuracy: 0.9305 - val_loss: 0.5656 - val_accuracy: 0.8636\n",
      "epoch 692\n",
      "44/44 [==============================] - 2s 43ms/step - loss: 0.1851 - accuracy: 0.9363 - val_loss: 0.5712 - val_accuracy: 0.8442\n",
      "epoch 693\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.1843 - accuracy: 0.9338 - val_loss: 0.5736 - val_accuracy: 0.8539\n",
      "epoch 694\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.1763 - accuracy: 0.9443 - val_loss: 0.6099 - val_accuracy: 0.8312\n",
      "epoch 695\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.1902 - accuracy: 0.9295 - val_loss: 0.5943 - val_accuracy: 0.8669\n",
      "epoch 696\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1958 - accuracy: 0.9309 - val_loss: 0.5737 - val_accuracy: 0.8539\n",
      "epoch 697\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1801 - accuracy: 0.9418 - val_loss: 0.6211 - val_accuracy: 0.8409\n",
      "epoch 698\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1917 - accuracy: 0.9284 - val_loss: 0.6663 - val_accuracy: 0.8377\n",
      "epoch 699\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1854 - accuracy: 0.9349 - val_loss: 0.5889 - val_accuracy: 0.8571\n",
      "epoch 700\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1915 - accuracy: 0.9313 - val_loss: 0.5970 - val_accuracy: 0.8344\n",
      "epoch 701\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.1903 - accuracy: 0.9298 - val_loss: 0.5786 - val_accuracy: 0.8571\n",
      "epoch 702\n",
      "44/44 [==============================] - 2s 47ms/step - loss: 0.1904 - accuracy: 0.9334 - val_loss: 0.5764 - val_accuracy: 0.8539\n",
      "epoch 703\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1803 - accuracy: 0.9374 - val_loss: 0.5740 - val_accuracy: 0.8539\n",
      "epoch 704\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1795 - accuracy: 0.9381 - val_loss: 0.5820 - val_accuracy: 0.8409\n",
      "epoch 705\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1857 - accuracy: 0.9345 - val_loss: 0.6240 - val_accuracy: 0.8312\n",
      "epoch 706\n",
      "44/44 [==============================] - 2s 42ms/step - loss: 0.1871 - accuracy: 0.9323 - val_loss: 0.5986 - val_accuracy: 0.8442\n",
      "epoch 707\n",
      "44/44 [==============================] - 2s 47ms/step - loss: 0.1981 - accuracy: 0.9269 - val_loss: 0.5665 - val_accuracy: 0.8571\n",
      "epoch 708\n",
      "44/44 [==============================] - 2s 46ms/step - loss: 0.1734 - accuracy: 0.9370 - val_loss: 0.5726 - val_accuracy: 0.8734\n",
      "epoch 709\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.1894 - accuracy: 0.9313 - val_loss: 0.5823 - val_accuracy: 0.8474\n",
      "epoch 710\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1759 - accuracy: 0.9370 - val_loss: 0.5473 - val_accuracy: 0.8571\n",
      "epoch 711\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.1759 - accuracy: 0.9399 - val_loss: 0.5811 - val_accuracy: 0.8571\n",
      "epoch 712\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1798 - accuracy: 0.9378 - val_loss: 0.6003 - val_accuracy: 0.8539\n",
      "epoch 713\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1756 - accuracy: 0.9396 - val_loss: 0.6000 - val_accuracy: 0.8377\n",
      "epoch 714\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1913 - accuracy: 0.9302 - val_loss: 0.6056 - val_accuracy: 0.8474\n",
      "epoch 715\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1761 - accuracy: 0.9410 - val_loss: 0.5775 - val_accuracy: 0.8377\n",
      "epoch 716\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1948 - accuracy: 0.9284 - val_loss: 0.6123 - val_accuracy: 0.8506\n",
      "epoch 717\n",
      "44/44 [==============================] - 2s 47ms/step - loss: 0.1906 - accuracy: 0.9360 - val_loss: 0.5666 - val_accuracy: 0.8571\n",
      "epoch 718\n",
      "44/44 [==============================] - 2s 43ms/step - loss: 0.1819 - accuracy: 0.9334 - val_loss: 0.6014 - val_accuracy: 0.8506\n",
      "epoch 719\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1860 - accuracy: 0.9367 - val_loss: 0.6061 - val_accuracy: 0.8312\n",
      "epoch 720\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1899 - accuracy: 0.9291 - val_loss: 0.6035 - val_accuracy: 0.8377\n",
      "epoch 721\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1858 - accuracy: 0.9316 - val_loss: 0.6044 - val_accuracy: 0.8377\n",
      "epoch 722\n",
      "44/44 [==============================] - 4s 88ms/step - loss: 0.1698 - accuracy: 0.9392 - val_loss: 0.5824 - val_accuracy: 0.8539\n",
      "epoch 723\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1722 - accuracy: 0.9403 - val_loss: 0.5649 - val_accuracy: 0.8474\n",
      "epoch 724\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.1873 - accuracy: 0.9323 - val_loss: 0.6156 - val_accuracy: 0.8506\n",
      "epoch 725\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1900 - accuracy: 0.9327 - val_loss: 0.5971 - val_accuracy: 0.8474\n",
      "epoch 726\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1825 - accuracy: 0.9367 - val_loss: 0.5863 - val_accuracy: 0.8506\n",
      "epoch 727\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1913 - accuracy: 0.9313 - val_loss: 0.6129 - val_accuracy: 0.8474\n",
      "epoch 728\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.1969 - accuracy: 0.9291 - val_loss: 0.6187 - val_accuracy: 0.8279\n",
      "epoch 729\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1824 - accuracy: 0.9331 - val_loss: 0.5929 - val_accuracy: 0.8377\n",
      "epoch 730\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1743 - accuracy: 0.9385 - val_loss: 0.6175 - val_accuracy: 0.8506\n",
      "epoch 731\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1841 - accuracy: 0.9349 - val_loss: 0.6055 - val_accuracy: 0.8506\n",
      "epoch 732\n",
      "44/44 [==============================] - 2s 47ms/step - loss: 0.2002 - accuracy: 0.9247 - val_loss: 0.5911 - val_accuracy: 0.8571\n",
      "epoch 733\n",
      "44/44 [==============================] - 2s 45ms/step - loss: 0.1743 - accuracy: 0.9414 - val_loss: 0.5918 - val_accuracy: 0.8539\n",
      "epoch 734\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1722 - accuracy: 0.9370 - val_loss: 0.6212 - val_accuracy: 0.8474\n",
      "epoch 735\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1759 - accuracy: 0.9407 - val_loss: 0.5764 - val_accuracy: 0.8539\n",
      "epoch 736\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1870 - accuracy: 0.9342 - val_loss: 0.6021 - val_accuracy: 0.8474\n",
      "epoch 737\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1788 - accuracy: 0.9338 - val_loss: 0.5930 - val_accuracy: 0.8442\n",
      "epoch 738\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1860 - accuracy: 0.9309 - val_loss: 0.6077 - val_accuracy: 0.8409\n",
      "epoch 739\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1721 - accuracy: 0.9418 - val_loss: 0.5996 - val_accuracy: 0.8442\n",
      "epoch 740\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.1651 - accuracy: 0.9465 - val_loss: 0.5888 - val_accuracy: 0.8506\n",
      "epoch 741\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.1743 - accuracy: 0.9334 - val_loss: 0.6084 - val_accuracy: 0.8506\n",
      "epoch 742\n",
      "44/44 [==============================] - 2s 49ms/step - loss: 0.1754 - accuracy: 0.9385 - val_loss: 0.6064 - val_accuracy: 0.8442\n",
      "epoch 743\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.1700 - accuracy: 0.9399 - val_loss: 0.5848 - val_accuracy: 0.8571\n",
      "epoch 744\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1980 - accuracy: 0.9269 - val_loss: 0.6348 - val_accuracy: 0.8312\n",
      "epoch 745\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.1766 - accuracy: 0.9360 - val_loss: 0.6342 - val_accuracy: 0.8442\n",
      "epoch 746\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1846 - accuracy: 0.9295 - val_loss: 0.6130 - val_accuracy: 0.8377\n",
      "epoch 747\n",
      "44/44 [==============================] - 2s 48ms/step - loss: 0.1866 - accuracy: 0.9305 - val_loss: 0.5777 - val_accuracy: 0.8571\n",
      "epoch 748\n",
      "44/44 [==============================] - 2s 48ms/step - loss: 0.1773 - accuracy: 0.9356 - val_loss: 0.6076 - val_accuracy: 0.8474\n",
      "epoch 749\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1756 - accuracy: 0.9410 - val_loss: 0.6059 - val_accuracy: 0.8312\n",
      "epoch 750\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.1805 - accuracy: 0.9334 - val_loss: 0.5871 - val_accuracy: 0.8279\n",
      "epoch 751\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.1759 - accuracy: 0.9381 - val_loss: 0.5896 - val_accuracy: 0.8636\n",
      "epoch 752\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1898 - accuracy: 0.9323 - val_loss: 0.6030 - val_accuracy: 0.8636\n",
      "epoch 753\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1773 - accuracy: 0.9374 - val_loss: 0.5987 - val_accuracy: 0.8669\n",
      "epoch 754\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.1776 - accuracy: 0.9403 - val_loss: 0.5836 - val_accuracy: 0.8506\n",
      "epoch 755\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1784 - accuracy: 0.9352 - val_loss: 0.5855 - val_accuracy: 0.8474\n",
      "epoch 756\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1779 - accuracy: 0.9385 - val_loss: 0.6367 - val_accuracy: 0.8571\n",
      "epoch 757\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.1762 - accuracy: 0.9385 - val_loss: 0.5983 - val_accuracy: 0.8474\n",
      "epoch 758\n",
      "44/44 [==============================] - 2s 47ms/step - loss: 0.1730 - accuracy: 0.9410 - val_loss: 0.5643 - val_accuracy: 0.8604\n",
      "epoch 759\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.1842 - accuracy: 0.9374 - val_loss: 0.6451 - val_accuracy: 0.8539\n",
      "epoch 760\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1822 - accuracy: 0.9298 - val_loss: 0.5972 - val_accuracy: 0.8636\n",
      "epoch 761\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1805 - accuracy: 0.9399 - val_loss: 0.5880 - val_accuracy: 0.8571\n",
      "epoch 762\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1697 - accuracy: 0.9403 - val_loss: 0.5923 - val_accuracy: 0.8506\n",
      "epoch 763\n",
      "44/44 [==============================] - 2s 45ms/step - loss: 0.1950 - accuracy: 0.9302 - val_loss: 0.6034 - val_accuracy: 0.8442\n",
      "epoch 764\n",
      "44/44 [==============================] - 2s 45ms/step - loss: 0.1768 - accuracy: 0.9367 - val_loss: 0.5923 - val_accuracy: 0.8506\n",
      "epoch 765\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.1694 - accuracy: 0.9399 - val_loss: 0.5958 - val_accuracy: 0.8506\n",
      "epoch 766\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.1658 - accuracy: 0.9425 - val_loss: 0.5942 - val_accuracy: 0.8571\n",
      "epoch 767\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1616 - accuracy: 0.9436 - val_loss: 0.6076 - val_accuracy: 0.8409\n",
      "epoch 768\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1700 - accuracy: 0.9410 - val_loss: 0.5984 - val_accuracy: 0.8604\n",
      "epoch 769\n",
      "44/44 [==============================] - 2s 42ms/step - loss: 0.1747 - accuracy: 0.9320 - val_loss: 0.6171 - val_accuracy: 0.8377\n",
      "epoch 770\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.1627 - accuracy: 0.9443 - val_loss: 0.6068 - val_accuracy: 0.8442\n",
      "epoch 771\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1697 - accuracy: 0.9407 - val_loss: 0.6117 - val_accuracy: 0.8571\n",
      "epoch 772\n",
      "44/44 [==============================] - 2s 45ms/step - loss: 0.1688 - accuracy: 0.9392 - val_loss: 0.6144 - val_accuracy: 0.8409\n",
      "epoch 773\n",
      "44/44 [==============================] - 2s 49ms/step - loss: 0.1728 - accuracy: 0.9378 - val_loss: 0.6383 - val_accuracy: 0.8312\n",
      "epoch 774\n",
      "44/44 [==============================] - 3s 58ms/step - loss: 0.1732 - accuracy: 0.9389 - val_loss: 0.5994 - val_accuracy: 0.8344\n",
      "epoch 775\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1652 - accuracy: 0.9425 - val_loss: 0.6132 - val_accuracy: 0.8312\n",
      "epoch 776\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1781 - accuracy: 0.9399 - val_loss: 0.6017 - val_accuracy: 0.8636\n",
      "epoch 777\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1698 - accuracy: 0.9410 - val_loss: 0.5984 - val_accuracy: 0.8571\n",
      "epoch 778\n",
      "44/44 [==============================] - 2s 35ms/step - loss: 0.1719 - accuracy: 0.9385 - val_loss: 0.6109 - val_accuracy: 0.8474\n",
      "epoch 779\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1826 - accuracy: 0.9287 - val_loss: 0.6023 - val_accuracy: 0.8539\n",
      "epoch 780\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1780 - accuracy: 0.9309 - val_loss: 0.6066 - val_accuracy: 0.8539\n",
      "epoch 781\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1710 - accuracy: 0.9396 - val_loss: 0.6092 - val_accuracy: 0.8604\n",
      "epoch 782\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.1714 - accuracy: 0.9399 - val_loss: 0.6150 - val_accuracy: 0.8377\n",
      "epoch 783\n",
      "44/44 [==============================] - 2s 46ms/step - loss: 0.1623 - accuracy: 0.9392 - val_loss: 0.5957 - val_accuracy: 0.8474\n",
      "epoch 784\n",
      "44/44 [==============================] - 2s 43ms/step - loss: 0.1575 - accuracy: 0.9493 - val_loss: 0.6130 - val_accuracy: 0.8474\n",
      "epoch 785\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1694 - accuracy: 0.9421 - val_loss: 0.6033 - val_accuracy: 0.8506\n",
      "epoch 786\n",
      "44/44 [==============================] - 2s 42ms/step - loss: 0.1664 - accuracy: 0.9410 - val_loss: 0.5895 - val_accuracy: 0.8571\n",
      "epoch 787\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1678 - accuracy: 0.9381 - val_loss: 0.6300 - val_accuracy: 0.8442\n",
      "epoch 788\n",
      "44/44 [==============================] - 2s 52ms/step - loss: 0.1666 - accuracy: 0.9389 - val_loss: 0.5879 - val_accuracy: 0.8604\n",
      "epoch 789\n",
      "44/44 [==============================] - 2s 51ms/step - loss: 0.1567 - accuracy: 0.9479 - val_loss: 0.5999 - val_accuracy: 0.8506\n",
      "epoch 790\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.1667 - accuracy: 0.9450 - val_loss: 0.5917 - val_accuracy: 0.8604\n",
      "epoch 791\n",
      "44/44 [==============================] - 2s 44ms/step - loss: 0.1530 - accuracy: 0.9465 - val_loss: 0.5928 - val_accuracy: 0.8442\n",
      "epoch 792\n",
      "44/44 [==============================] - 2s 42ms/step - loss: 0.1663 - accuracy: 0.9450 - val_loss: 0.5892 - val_accuracy: 0.8409\n",
      "epoch 793\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.1720 - accuracy: 0.9421 - val_loss: 0.6120 - val_accuracy: 0.8442\n",
      "epoch 794\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.1661 - accuracy: 0.9399 - val_loss: 0.5968 - val_accuracy: 0.8539\n",
      "epoch 795\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.1742 - accuracy: 0.9363 - val_loss: 0.6412 - val_accuracy: 0.8312\n",
      "epoch 796\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.1664 - accuracy: 0.9385 - val_loss: 0.6054 - val_accuracy: 0.8474\n",
      "epoch 797\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.1703 - accuracy: 0.9399 - val_loss: 0.6267 - val_accuracy: 0.8474\n",
      "epoch 798\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.1738 - accuracy: 0.9399 - val_loss: 0.6190 - val_accuracy: 0.8474\n",
      "epoch 799\n",
      "44/44 [==============================] - 2s 51ms/step - loss: 0.1720 - accuracy: 0.9385 - val_loss: 0.6312 - val_accuracy: 0.8474\n",
      "epoch 800\n",
      "44/44 [==============================] - 2s 46ms/step - loss: 0.1682 - accuracy: 0.9428 - val_loss: 0.5921 - val_accuracy: 0.8539\n",
      "epoch 801\n",
      "44/44 [==============================] - 2s 43ms/step - loss: 0.1577 - accuracy: 0.9454 - val_loss: 0.5958 - val_accuracy: 0.8506\n",
      "epoch 802\n",
      "44/44 [==============================] - 3s 59ms/step - loss: 0.1653 - accuracy: 0.9418 - val_loss: 0.6023 - val_accuracy: 0.8442\n",
      "epoch 803\n",
      "44/44 [==============================] - 2s 45ms/step - loss: 0.1578 - accuracy: 0.9450 - val_loss: 0.5941 - val_accuracy: 0.8571\n",
      "epoch 804\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.1561 - accuracy: 0.9450 - val_loss: 0.6111 - val_accuracy: 0.8474\n",
      "epoch 805\n",
      "44/44 [==============================] - 2s 43ms/step - loss: 0.1599 - accuracy: 0.9425 - val_loss: 0.6442 - val_accuracy: 0.8247\n",
      "epoch 806\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1558 - accuracy: 0.9461 - val_loss: 0.6344 - val_accuracy: 0.8409\n",
      "epoch 807\n",
      "44/44 [==============================] - 2s 44ms/step - loss: 0.1643 - accuracy: 0.9446 - val_loss: 0.6047 - val_accuracy: 0.8636\n",
      "epoch 808\n",
      "44/44 [==============================] - 2s 44ms/step - loss: 0.1581 - accuracy: 0.9468 - val_loss: 0.7060 - val_accuracy: 0.8182\n",
      "epoch 809\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.1705 - accuracy: 0.9352 - val_loss: 0.6230 - val_accuracy: 0.8506\n",
      "epoch 810\n",
      "44/44 [==============================] - 2s 44ms/step - loss: 0.1677 - accuracy: 0.9370 - val_loss: 0.6119 - val_accuracy: 0.8377\n",
      "epoch 811\n",
      "44/44 [==============================] - 2s 49ms/step - loss: 0.1590 - accuracy: 0.9425 - val_loss: 0.6120 - val_accuracy: 0.8506\n",
      "epoch 812\n",
      "44/44 [==============================] - 2s 48ms/step - loss: 0.1635 - accuracy: 0.9414 - val_loss: 0.6493 - val_accuracy: 0.8344\n",
      "epoch 813\n",
      "44/44 [==============================] - 2s 44ms/step - loss: 0.1773 - accuracy: 0.9345 - val_loss: 0.6191 - val_accuracy: 0.8409\n",
      "epoch 814\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1608 - accuracy: 0.9439 - val_loss: 0.5954 - val_accuracy: 0.8604\n",
      "epoch 815\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1616 - accuracy: 0.9439 - val_loss: 0.5982 - val_accuracy: 0.8669\n",
      "epoch 816\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1516 - accuracy: 0.9461 - val_loss: 0.6757 - val_accuracy: 0.8377\n",
      "epoch 817\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1958 - accuracy: 0.9298 - val_loss: 0.6413 - val_accuracy: 0.8344\n",
      "epoch 818\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1713 - accuracy: 0.9370 - val_loss: 0.6737 - val_accuracy: 0.8247\n",
      "epoch 819\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1734 - accuracy: 0.9396 - val_loss: 0.6301 - val_accuracy: 0.8377\n",
      "epoch 820\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1601 - accuracy: 0.9457 - val_loss: 0.6131 - val_accuracy: 0.8279\n",
      "epoch 821\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.1621 - accuracy: 0.9450 - val_loss: 0.6108 - val_accuracy: 0.8571\n",
      "epoch 822\n",
      "44/44 [==============================] - 2s 45ms/step - loss: 0.1640 - accuracy: 0.9389 - val_loss: 0.6109 - val_accuracy: 0.8669\n",
      "epoch 823\n",
      "44/44 [==============================] - 2s 44ms/step - loss: 0.1518 - accuracy: 0.9533 - val_loss: 0.5936 - val_accuracy: 0.8604\n",
      "epoch 824\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1649 - accuracy: 0.9399 - val_loss: 0.6226 - val_accuracy: 0.8409\n",
      "epoch 825\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1767 - accuracy: 0.9352 - val_loss: 0.6180 - val_accuracy: 0.8539\n",
      "epoch 826\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1661 - accuracy: 0.9443 - val_loss: 0.6572 - val_accuracy: 0.8279\n",
      "epoch 827\n",
      "44/44 [==============================] - 2s 48ms/step - loss: 0.1580 - accuracy: 0.9439 - val_loss: 0.5960 - val_accuracy: 0.8539\n",
      "epoch 828\n",
      "44/44 [==============================] - 2s 44ms/step - loss: 0.1520 - accuracy: 0.9457 - val_loss: 0.6387 - val_accuracy: 0.8442\n",
      "epoch 829\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1590 - accuracy: 0.9381 - val_loss: 0.6132 - val_accuracy: 0.8409\n",
      "epoch 830\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1584 - accuracy: 0.9479 - val_loss: 0.6216 - val_accuracy: 0.8539\n",
      "epoch 831\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1620 - accuracy: 0.9461 - val_loss: 0.6412 - val_accuracy: 0.8506\n",
      "epoch 832\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1523 - accuracy: 0.9483 - val_loss: 0.6254 - val_accuracy: 0.8506\n",
      "epoch 833\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1557 - accuracy: 0.9443 - val_loss: 0.5936 - val_accuracy: 0.8604\n",
      "epoch 834\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1508 - accuracy: 0.9483 - val_loss: 0.6373 - val_accuracy: 0.8474\n",
      "epoch 835\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1607 - accuracy: 0.9410 - val_loss: 0.6304 - val_accuracy: 0.8506\n",
      "epoch 836\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1593 - accuracy: 0.9443 - val_loss: 0.5976 - val_accuracy: 0.8571\n",
      "epoch 837\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.1553 - accuracy: 0.9454 - val_loss: 0.6117 - val_accuracy: 0.8474\n",
      "epoch 838\n",
      "44/44 [==============================] - 2s 49ms/step - loss: 0.1646 - accuracy: 0.9403 - val_loss: 0.5903 - val_accuracy: 0.8474\n",
      "epoch 839\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.1705 - accuracy: 0.9418 - val_loss: 0.6369 - val_accuracy: 0.8312\n",
      "epoch 840\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1544 - accuracy: 0.9450 - val_loss: 0.6325 - val_accuracy: 0.8474\n",
      "epoch 841\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1691 - accuracy: 0.9457 - val_loss: 0.6335 - val_accuracy: 0.8409\n",
      "epoch 842\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.1558 - accuracy: 0.9475 - val_loss: 0.6136 - val_accuracy: 0.8442\n",
      "epoch 843\n",
      "44/44 [==============================] - 2s 45ms/step - loss: 0.1597 - accuracy: 0.9454 - val_loss: 0.6280 - val_accuracy: 0.8539\n",
      "epoch 844\n",
      "44/44 [==============================] - 2s 43ms/step - loss: 0.1554 - accuracy: 0.9439 - val_loss: 0.5917 - val_accuracy: 0.8474\n",
      "epoch 845\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.1475 - accuracy: 0.9493 - val_loss: 0.6402 - val_accuracy: 0.8539\n",
      "epoch 846\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1876 - accuracy: 0.9320 - val_loss: 0.6638 - val_accuracy: 0.8442\n",
      "epoch 847\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1854 - accuracy: 0.9302 - val_loss: 0.6311 - val_accuracy: 0.8442\n",
      "epoch 848\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.1545 - accuracy: 0.9461 - val_loss: 0.6198 - val_accuracy: 0.8474\n",
      "epoch 849\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.1491 - accuracy: 0.9508 - val_loss: 0.6226 - val_accuracy: 0.8409\n",
      "epoch 850\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1528 - accuracy: 0.9483 - val_loss: 0.6139 - val_accuracy: 0.8571\n",
      "epoch 851\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.1534 - accuracy: 0.9497 - val_loss: 0.5976 - val_accuracy: 0.8506\n",
      "epoch 852\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1527 - accuracy: 0.9486 - val_loss: 0.6239 - val_accuracy: 0.8442\n",
      "epoch 853\n",
      "44/44 [==============================] - 2s 50ms/step - loss: 0.1595 - accuracy: 0.9421 - val_loss: 0.6153 - val_accuracy: 0.8474\n",
      "epoch 854\n",
      "44/44 [==============================] - 3s 65ms/step - loss: 0.1575 - accuracy: 0.9446 - val_loss: 0.6367 - val_accuracy: 0.8506\n",
      "epoch 855\n",
      "44/44 [==============================] - 3s 61ms/step - loss: 0.1579 - accuracy: 0.9446 - val_loss: 0.6551 - val_accuracy: 0.8377\n",
      "epoch 856\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.1595 - accuracy: 0.9436 - val_loss: 0.6278 - val_accuracy: 0.8442\n",
      "epoch 857\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1646 - accuracy: 0.9392 - val_loss: 0.6183 - val_accuracy: 0.8604\n",
      "epoch 858\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.1457 - accuracy: 0.9493 - val_loss: 0.6323 - val_accuracy: 0.8312\n",
      "epoch 859\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.1671 - accuracy: 0.9407 - val_loss: 0.5982 - val_accuracy: 0.8636\n",
      "epoch 860\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.1651 - accuracy: 0.9392 - val_loss: 0.6106 - val_accuracy: 0.8442\n",
      "epoch 861\n",
      "44/44 [==============================] - 2s 50ms/step - loss: 0.1545 - accuracy: 0.9490 - val_loss: 0.6024 - val_accuracy: 0.8539\n",
      "epoch 862\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.1438 - accuracy: 0.9544 - val_loss: 0.6068 - val_accuracy: 0.8539\n",
      "epoch 863\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1474 - accuracy: 0.9504 - val_loss: 0.6195 - val_accuracy: 0.8474\n",
      "epoch 864\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.1428 - accuracy: 0.9501 - val_loss: 0.6028 - val_accuracy: 0.8506\n",
      "epoch 865\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1477 - accuracy: 0.9475 - val_loss: 0.5985 - val_accuracy: 0.8571\n",
      "epoch 866\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1551 - accuracy: 0.9475 - val_loss: 0.6724 - val_accuracy: 0.8377\n",
      "epoch 867\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1552 - accuracy: 0.9436 - val_loss: 0.6286 - val_accuracy: 0.8474\n",
      "epoch 868\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1533 - accuracy: 0.9454 - val_loss: 0.6269 - val_accuracy: 0.8279\n",
      "epoch 869\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1507 - accuracy: 0.9483 - val_loss: 0.6244 - val_accuracy: 0.8474\n",
      "epoch 870\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.1593 - accuracy: 0.9436 - val_loss: 0.6048 - val_accuracy: 0.8636\n",
      "epoch 871\n",
      "44/44 [==============================] - 2s 45ms/step - loss: 0.1539 - accuracy: 0.9457 - val_loss: 0.6274 - val_accuracy: 0.8506\n",
      "epoch 872\n",
      "44/44 [==============================] - 2s 45ms/step - loss: 0.1507 - accuracy: 0.9443 - val_loss: 0.6471 - val_accuracy: 0.8377\n",
      "epoch 873\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1512 - accuracy: 0.9457 - val_loss: 0.6233 - val_accuracy: 0.8409\n",
      "epoch 874\n",
      "44/44 [==============================] - 2s 35ms/step - loss: 0.1534 - accuracy: 0.9472 - val_loss: 0.6135 - val_accuracy: 0.8539\n",
      "epoch 875\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1469 - accuracy: 0.9475 - val_loss: 0.6425 - val_accuracy: 0.8442\n",
      "epoch 876\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.1463 - accuracy: 0.9501 - val_loss: 0.6273 - val_accuracy: 0.8312\n",
      "epoch 877\n",
      "44/44 [==============================] - 2s 47ms/step - loss: 0.1478 - accuracy: 0.9530 - val_loss: 0.6293 - val_accuracy: 0.8506\n",
      "epoch 878\n",
      "44/44 [==============================] - 2s 35ms/step - loss: 0.1461 - accuracy: 0.9519 - val_loss: 0.6499 - val_accuracy: 0.8377\n",
      "epoch 879\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1502 - accuracy: 0.9461 - val_loss: 0.6070 - val_accuracy: 0.8409\n",
      "epoch 880\n",
      "44/44 [==============================] - 2s 35ms/step - loss: 0.1439 - accuracy: 0.9475 - val_loss: 0.6610 - val_accuracy: 0.8409\n",
      "epoch 881\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1529 - accuracy: 0.9512 - val_loss: 0.6133 - val_accuracy: 0.8506\n",
      "epoch 882\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1474 - accuracy: 0.9497 - val_loss: 0.6143 - val_accuracy: 0.8442\n",
      "epoch 883\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1563 - accuracy: 0.9454 - val_loss: 0.6427 - val_accuracy: 0.8474\n",
      "epoch 884\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1450 - accuracy: 0.9501 - val_loss: 0.6532 - val_accuracy: 0.8442\n",
      "epoch 885\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1572 - accuracy: 0.9461 - val_loss: 0.6487 - val_accuracy: 0.8377\n",
      "epoch 886\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1476 - accuracy: 0.9454 - val_loss: 0.6101 - val_accuracy: 0.8506\n",
      "epoch 887\n",
      "44/44 [==============================] - 2s 43ms/step - loss: 0.1464 - accuracy: 0.9522 - val_loss: 0.6172 - val_accuracy: 0.8571\n",
      "epoch 888\n",
      "44/44 [==============================] - 2s 47ms/step - loss: 0.1508 - accuracy: 0.9475 - val_loss: 0.6674 - val_accuracy: 0.8312\n",
      "epoch 889\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1544 - accuracy: 0.9454 - val_loss: 0.6315 - val_accuracy: 0.8409\n",
      "epoch 890\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.1463 - accuracy: 0.9479 - val_loss: 0.6696 - val_accuracy: 0.8312\n",
      "epoch 891\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1542 - accuracy: 0.9446 - val_loss: 0.6352 - val_accuracy: 0.8442\n",
      "epoch 892\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.1510 - accuracy: 0.9465 - val_loss: 0.6247 - val_accuracy: 0.8506\n",
      "epoch 893\n",
      "44/44 [==============================] - 2s 48ms/step - loss: 0.1394 - accuracy: 0.9515 - val_loss: 0.6324 - val_accuracy: 0.8571\n",
      "epoch 894\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.1457 - accuracy: 0.9515 - val_loss: 0.6075 - val_accuracy: 0.8571\n",
      "epoch 895\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1531 - accuracy: 0.9443 - val_loss: 0.6064 - val_accuracy: 0.8539\n",
      "epoch 896\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1442 - accuracy: 0.9533 - val_loss: 0.6492 - val_accuracy: 0.8377\n",
      "epoch 897\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1296 - accuracy: 0.9573 - val_loss: 0.6284 - val_accuracy: 0.8539\n",
      "epoch 898\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.1414 - accuracy: 0.9508 - val_loss: 0.6638 - val_accuracy: 0.8409\n",
      "epoch 899\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1511 - accuracy: 0.9439 - val_loss: 0.6329 - val_accuracy: 0.8409\n",
      "epoch 900\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1491 - accuracy: 0.9508 - val_loss: 0.6381 - val_accuracy: 0.8539\n",
      "epoch 901\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1429 - accuracy: 0.9512 - val_loss: 0.6540 - val_accuracy: 0.8442\n",
      "epoch 902\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1636 - accuracy: 0.9418 - val_loss: 0.6394 - val_accuracy: 0.8539\n",
      "epoch 903\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1444 - accuracy: 0.9490 - val_loss: 0.6632 - val_accuracy: 0.8506\n",
      "epoch 904\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.1529 - accuracy: 0.9483 - val_loss: 0.6232 - val_accuracy: 0.8539\n",
      "epoch 905\n",
      "44/44 [==============================] - 2s 48ms/step - loss: 0.1486 - accuracy: 0.9436 - val_loss: 0.6263 - val_accuracy: 0.8377\n",
      "epoch 906\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.1550 - accuracy: 0.9410 - val_loss: 0.6472 - val_accuracy: 0.8442\n",
      "epoch 907\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1441 - accuracy: 0.9486 - val_loss: 0.6251 - val_accuracy: 0.8571\n",
      "epoch 908\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1526 - accuracy: 0.9446 - val_loss: 0.6514 - val_accuracy: 0.8604\n",
      "epoch 909\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1364 - accuracy: 0.9519 - val_loss: 0.6304 - val_accuracy: 0.8409\n",
      "epoch 910\n",
      "44/44 [==============================] - 2s 45ms/step - loss: 0.1432 - accuracy: 0.9472 - val_loss: 0.6427 - val_accuracy: 0.8474\n",
      "epoch 911\n",
      "44/44 [==============================] - 2s 45ms/step - loss: 0.1484 - accuracy: 0.9472 - val_loss: 0.6039 - val_accuracy: 0.8409\n",
      "epoch 912\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1383 - accuracy: 0.9533 - val_loss: 0.6231 - val_accuracy: 0.8409\n",
      "epoch 913\n",
      "44/44 [==============================] - 2s 45ms/step - loss: 0.1398 - accuracy: 0.9461 - val_loss: 0.6252 - val_accuracy: 0.8604\n",
      "epoch 914\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.1421 - accuracy: 0.9541 - val_loss: 0.6583 - val_accuracy: 0.8539\n",
      "epoch 915\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.1479 - accuracy: 0.9443 - val_loss: 0.6376 - val_accuracy: 0.8474\n",
      "epoch 916\n",
      "44/44 [==============================] - 2s 35ms/step - loss: 0.1386 - accuracy: 0.9501 - val_loss: 0.6257 - val_accuracy: 0.8571\n",
      "epoch 917\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1374 - accuracy: 0.9515 - val_loss: 0.6623 - val_accuracy: 0.8506\n",
      "epoch 918\n",
      "44/44 [==============================] - 2s 35ms/step - loss: 0.1420 - accuracy: 0.9490 - val_loss: 0.6520 - val_accuracy: 0.8377\n",
      "epoch 919\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1461 - accuracy: 0.9486 - val_loss: 0.6421 - val_accuracy: 0.8506\n",
      "epoch 920\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1378 - accuracy: 0.9515 - val_loss: 0.6206 - val_accuracy: 0.8539\n",
      "epoch 921\n",
      "44/44 [==============================] - 2s 35ms/step - loss: 0.1343 - accuracy: 0.9522 - val_loss: 0.6388 - val_accuracy: 0.8604\n",
      "epoch 922\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.1422 - accuracy: 0.9457 - val_loss: 0.6203 - val_accuracy: 0.8636\n",
      "epoch 923\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1490 - accuracy: 0.9483 - val_loss: 0.6535 - val_accuracy: 0.8442\n",
      "epoch 924\n",
      "44/44 [==============================] - 1866s 112ms/step - loss: 0.1454 - accuracy: 0.9475 - val_loss: 0.6524 - val_accuracy: 0.8474\n",
      "epoch 925\n",
      "44/44 [==============================] - 3s 80ms/step - loss: 0.1328 - accuracy: 0.9544 - val_loss: 0.6714 - val_accuracy: 0.8506\n",
      "epoch 926\n",
      "44/44 [==============================] - 3s 66ms/step - loss: 0.1578 - accuracy: 0.9410 - val_loss: 0.6380 - val_accuracy: 0.8539\n",
      "epoch 927\n",
      "44/44 [==============================] - 3s 64ms/step - loss: 0.1554 - accuracy: 0.9432 - val_loss: 0.6768 - val_accuracy: 0.8539\n",
      "epoch 928\n",
      "44/44 [==============================] - 2s 55ms/step - loss: 0.1587 - accuracy: 0.9389 - val_loss: 0.6510 - val_accuracy: 0.8474\n",
      "epoch 929\n",
      "44/44 [==============================] - 2s 52ms/step - loss: 0.1321 - accuracy: 0.9559 - val_loss: 0.6447 - val_accuracy: 0.8442\n",
      "epoch 930\n",
      "44/44 [==============================] - 2s 43ms/step - loss: 0.1421 - accuracy: 0.9486 - val_loss: 0.6834 - val_accuracy: 0.8539\n",
      "epoch 931\n",
      "44/44 [==============================] - 2s 43ms/step - loss: 0.1507 - accuracy: 0.9465 - val_loss: 0.6211 - val_accuracy: 0.8571\n",
      "epoch 932\n",
      "44/44 [==============================] - 2s 49ms/step - loss: 0.1452 - accuracy: 0.9501 - val_loss: 0.6661 - val_accuracy: 0.8442\n",
      "epoch 933\n",
      "44/44 [==============================] - 2s 47ms/step - loss: 0.1483 - accuracy: 0.9450 - val_loss: 0.6694 - val_accuracy: 0.8377\n",
      "epoch 934\n",
      "44/44 [==============================] - 2s 44ms/step - loss: 0.1393 - accuracy: 0.9544 - val_loss: 0.6333 - val_accuracy: 0.8539\n",
      "epoch 935\n",
      "44/44 [==============================] - 2s 50ms/step - loss: 0.1319 - accuracy: 0.9559 - val_loss: 0.6308 - val_accuracy: 0.8571\n",
      "epoch 936\n",
      "44/44 [==============================] - 2s 56ms/step - loss: 0.1375 - accuracy: 0.9512 - val_loss: 0.6337 - val_accuracy: 0.8539\n",
      "epoch 937\n",
      "44/44 [==============================] - 2s 43ms/step - loss: 0.1428 - accuracy: 0.9512 - val_loss: 0.7053 - val_accuracy: 0.8344\n",
      "epoch 938\n",
      "44/44 [==============================] - 2s 43ms/step - loss: 0.1435 - accuracy: 0.9483 - val_loss: 0.7034 - val_accuracy: 0.8312\n",
      "epoch 939\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.1417 - accuracy: 0.9486 - val_loss: 0.6672 - val_accuracy: 0.8539\n",
      "epoch 940\n",
      "44/44 [==============================] - 2s 46ms/step - loss: 0.1533 - accuracy: 0.9432 - val_loss: 0.6429 - val_accuracy: 0.8539\n",
      "epoch 941\n",
      "44/44 [==============================] - 2s 52ms/step - loss: 0.1395 - accuracy: 0.9537 - val_loss: 0.6532 - val_accuracy: 0.8474\n",
      "epoch 942\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.1361 - accuracy: 0.9530 - val_loss: 0.6811 - val_accuracy: 0.8442\n",
      "epoch 943\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.1421 - accuracy: 0.9490 - val_loss: 0.6381 - val_accuracy: 0.8506\n",
      "epoch 944\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.1378 - accuracy: 0.9548 - val_loss: 0.6364 - val_accuracy: 0.8474\n",
      "epoch 945\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.1296 - accuracy: 0.9580 - val_loss: 0.6396 - val_accuracy: 0.8604\n",
      "epoch 946\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1384 - accuracy: 0.9544 - val_loss: 0.6584 - val_accuracy: 0.8312\n",
      "epoch 947\n",
      "44/44 [==============================] - 2s 50ms/step - loss: 0.1378 - accuracy: 0.9530 - val_loss: 0.6274 - val_accuracy: 0.8604\n",
      "epoch 948\n",
      "44/44 [==============================] - 2s 49ms/step - loss: 0.1452 - accuracy: 0.9497 - val_loss: 0.6586 - val_accuracy: 0.8506\n",
      "epoch 949\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.1411 - accuracy: 0.9526 - val_loss: 0.6472 - val_accuracy: 0.8539\n",
      "epoch 950\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.1370 - accuracy: 0.9526 - val_loss: 0.6550 - val_accuracy: 0.8474\n",
      "epoch 951\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.1300 - accuracy: 0.9512 - val_loss: 0.6813 - val_accuracy: 0.8506\n",
      "epoch 952\n",
      "44/44 [==============================] - 2s 43ms/step - loss: 0.1332 - accuracy: 0.9522 - val_loss: 0.6696 - val_accuracy: 0.8474\n",
      "epoch 953\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.1419 - accuracy: 0.9522 - val_loss: 0.6456 - val_accuracy: 0.8571\n",
      "epoch 954\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.1321 - accuracy: 0.9533 - val_loss: 0.6358 - val_accuracy: 0.8409\n",
      "epoch 955\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.1387 - accuracy: 0.9515 - val_loss: 0.6608 - val_accuracy: 0.8539\n",
      "epoch 956\n",
      "44/44 [==============================] - 2s 52ms/step - loss: 0.1390 - accuracy: 0.9544 - val_loss: 0.6549 - val_accuracy: 0.8669\n",
      "epoch 957\n",
      "44/44 [==============================] - 2s 48ms/step - loss: 0.1428 - accuracy: 0.9519 - val_loss: 0.6853 - val_accuracy: 0.8571\n",
      "epoch 958\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.1344 - accuracy: 0.9555 - val_loss: 0.6890 - val_accuracy: 0.8377\n",
      "epoch 959\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.1440 - accuracy: 0.9490 - val_loss: 0.6906 - val_accuracy: 0.8539\n",
      "epoch 960\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.1355 - accuracy: 0.9497 - val_loss: 0.6527 - val_accuracy: 0.8474\n",
      "epoch 961\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.1277 - accuracy: 0.9588 - val_loss: 0.6677 - val_accuracy: 0.8474\n",
      "epoch 962\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.1334 - accuracy: 0.9559 - val_loss: 0.6578 - val_accuracy: 0.8571\n",
      "epoch 963\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.1297 - accuracy: 0.9526 - val_loss: 0.6491 - val_accuracy: 0.8636\n",
      "epoch 964\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.1307 - accuracy: 0.9562 - val_loss: 0.6666 - val_accuracy: 0.8474\n",
      "epoch 965\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.1347 - accuracy: 0.9559 - val_loss: 0.6844 - val_accuracy: 0.8409\n",
      "epoch 966\n",
      "44/44 [==============================] - 2s 49ms/step - loss: 0.1262 - accuracy: 0.9548 - val_loss: 0.6621 - val_accuracy: 0.8377\n",
      "epoch 967\n",
      "44/44 [==============================] - 2s 45ms/step - loss: 0.1398 - accuracy: 0.9504 - val_loss: 0.6786 - val_accuracy: 0.8377\n",
      "epoch 968\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.1281 - accuracy: 0.9569 - val_loss: 0.7030 - val_accuracy: 0.8377\n",
      "epoch 969\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.1325 - accuracy: 0.9551 - val_loss: 0.6342 - val_accuracy: 0.8571\n",
      "epoch 970\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1348 - accuracy: 0.9548 - val_loss: 0.6635 - val_accuracy: 0.8506\n",
      "epoch 971\n",
      "44/44 [==============================] - 2s 48ms/step - loss: 0.1329 - accuracy: 0.9493 - val_loss: 0.6848 - val_accuracy: 0.8506\n",
      "epoch 972\n",
      "44/44 [==============================] - 2s 48ms/step - loss: 0.1320 - accuracy: 0.9544 - val_loss: 0.6648 - val_accuracy: 0.8571\n",
      "epoch 973\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1278 - accuracy: 0.9577 - val_loss: 0.6465 - val_accuracy: 0.8506\n",
      "epoch 974\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.1288 - accuracy: 0.9537 - val_loss: 0.6555 - val_accuracy: 0.8442\n",
      "epoch 975\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1255 - accuracy: 0.9551 - val_loss: 0.6502 - val_accuracy: 0.8539\n",
      "epoch 976\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1307 - accuracy: 0.9530 - val_loss: 0.6996 - val_accuracy: 0.8279\n",
      "epoch 977\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.1396 - accuracy: 0.9548 - val_loss: 0.6884 - val_accuracy: 0.8442\n",
      "epoch 978\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.1346 - accuracy: 0.9533 - val_loss: 0.6468 - val_accuracy: 0.8571\n",
      "epoch 979\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1263 - accuracy: 0.9580 - val_loss: 0.7271 - val_accuracy: 0.8344\n",
      "epoch 980\n",
      "44/44 [==============================] - 2s 48ms/step - loss: 0.1489 - accuracy: 0.9465 - val_loss: 0.6699 - val_accuracy: 0.8571\n",
      "epoch 981\n",
      "44/44 [==============================] - 2s 48ms/step - loss: 0.1254 - accuracy: 0.9584 - val_loss: 0.6598 - val_accuracy: 0.8571\n",
      "epoch 982\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.1235 - accuracy: 0.9548 - val_loss: 0.6346 - val_accuracy: 0.8636\n",
      "epoch 983\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.1290 - accuracy: 0.9559 - val_loss: 0.6471 - val_accuracy: 0.8604\n",
      "epoch 984\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1286 - accuracy: 0.9566 - val_loss: 0.6624 - val_accuracy: 0.8506\n",
      "epoch 985\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.1299 - accuracy: 0.9541 - val_loss: 0.6787 - val_accuracy: 0.8571\n",
      "epoch 986\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1267 - accuracy: 0.9562 - val_loss: 0.6384 - val_accuracy: 0.8604\n",
      "epoch 987\n",
      "44/44 [==============================] - 2s 38ms/step - loss: 0.1312 - accuracy: 0.9548 - val_loss: 0.6702 - val_accuracy: 0.8506\n",
      "epoch 988\n",
      "44/44 [==============================] - 2s 36ms/step - loss: 0.1369 - accuracy: 0.9537 - val_loss: 0.6472 - val_accuracy: 0.8571\n",
      "epoch 989\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.1370 - accuracy: 0.9483 - val_loss: 0.6604 - val_accuracy: 0.8377\n",
      "epoch 990\n",
      "44/44 [==============================] - 2s 42ms/step - loss: 0.1223 - accuracy: 0.9595 - val_loss: 0.6525 - val_accuracy: 0.8604\n",
      "epoch 991\n",
      "44/44 [==============================] - 2s 51ms/step - loss: 0.1240 - accuracy: 0.9584 - val_loss: 0.6708 - val_accuracy: 0.8474\n",
      "epoch 992\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.1251 - accuracy: 0.9620 - val_loss: 0.6666 - val_accuracy: 0.8506\n",
      "epoch 993\n",
      "44/44 [==============================] - 2s 39ms/step - loss: 0.1322 - accuracy: 0.9551 - val_loss: 0.6958 - val_accuracy: 0.8344\n",
      "epoch 994\n",
      "44/44 [==============================] - 2s 37ms/step - loss: 0.1290 - accuracy: 0.9526 - val_loss: 0.6578 - val_accuracy: 0.8571\n",
      "epoch 995\n",
      "44/44 [==============================] - 2s 46ms/step - loss: 0.1288 - accuracy: 0.9533 - val_loss: 0.7260 - val_accuracy: 0.8442\n",
      "epoch 996\n",
      "44/44 [==============================] - 2s 46ms/step - loss: 0.1486 - accuracy: 0.9443 - val_loss: 0.6727 - val_accuracy: 0.8377\n",
      "epoch 997\n",
      "44/44 [==============================] - 2s 41ms/step - loss: 0.1329 - accuracy: 0.9515 - val_loss: 0.6544 - val_accuracy: 0.8506\n",
      "epoch 998\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.1366 - accuracy: 0.9551 - val_loss: 0.6757 - val_accuracy: 0.8571\n",
      "epoch 999\n",
      "44/44 [==============================] - 2s 40ms/step - loss: 0.1272 - accuracy: 0.9537 - val_loss: 0.6762 - val_accuracy: 0.8377\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(epoch_save+1, 1000): #change 100 to a larger number if necessary\n",
    "    print('epoch', epoch)\n",
    "    #set epochs=1\n",
    "    history=model.fit(X_train, Y_train, batch_size=64, epochs=1, validation_data=(X_val, Y_val))\n",
    "    loss_train_list.extend(history.history['loss'])\n",
    "    loss_val_list.extend(history.history['val_loss'])\n",
    "    acc_train_list.extend(history.history['accuracy'])\n",
    "    acc_val_list.extend(history.history['val_accuracy'])\n",
    "    #save the model to a file \n",
    "    model.save('ECG_Keras_sCE_e'+str(epoch)+'.keras')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XEVFA7sxOZjS"
   },
   "source": [
    "### Train the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5lz4J2sMOZjS",
    "outputId": "d73c4307-9fb8-42f7-96ed-d3a2c7e2fc45",
    "scrolled": false
   },
   "source": [
    "for epoch in range(epoch_save+1, 100): #change 100 to a larger number if necessary\n",
    "    print('epoch', epoch)\n",
    "    #set epochs=1\n",
    "    history=model.fit(X_train, Y_train_encoded, batch_size=64, epochs=1, validation_data=(X_val, Y_val_encoded))\n",
    "    loss_train_list.extend(history.history['loss'])\n",
    "    loss_val_list.extend(history.history['val_loss'])\n",
    "    acc_train_list.extend(history.history['accuracy'])\n",
    "    acc_val_list.extend(history.history['val_accuracy'])\n",
    "    #save the model to a file \n",
    "    model.save('ECG_Keras_sCE_e'+str(epoch)+'.keras')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FBSDSgFfOZjT"
   },
   "source": [
    "### Plot training loss vs epoch and validation loss vs epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-KRkqHpuOZjT",
    "outputId": "3d87e5df-b3ee-4c8a-f48f-c3c318946105"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])"
      ]
     },
     "execution_count": 341,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history.history.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 393
    },
    "id": "xsc9XDhROZjT",
    "outputId": "16fa3447-738b-4b6f-8598-e5b44a2cad95"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(12,6))\n",
    "ax[0].plot(np.arange(0,len(loss_train_list)), loss_train_list, '-b', label='training loss')\n",
    "ax[0].plot(np.arange(0,len(loss_val_list)), loss_val_list, '-r', label='validation loss')\n",
    "ax[0].set_xlabel('epoch',fontsize=16)\n",
    "ax[0].legend(fontsize=16)\n",
    "ax[0].grid(True)\n",
    "ax[1].plot(np.arange(0,len(acc_train_list)), acc_train_list, '-b', label='training accuracy')\n",
    "ax[1].plot(np.arange(0,len(acc_val_list)), acc_val_list, '-r', label='validation accuracy')\n",
    "ax[1].set_xlabel('epoch',fontsize=16)\n",
    "ax[1].legend(fontsize=16)\n",
    "ax[1].grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tRXmqXsiOZjU"
   },
   "source": [
    "### Test the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_R3FxdyLt-Rd",
    "outputId": "4e4c44e0-9816-4a4e-a925-1855d2f9a273"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "827"
      ]
     },
     "execution_count": 343,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#load the best model\n",
    "best_epoch=np.argmax(acc_val_list)\n",
    "best_epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {
    "id": "GMUgAglLuP1l"
   },
   "outputs": [],
   "source": [
    "import tensorflow\n",
    "model = tensorflow.keras.models.load_model(\"ECG_Keras_sCE_e\"+str(best_epoch)+\".keras\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kvtKs2SOOZjU",
    "outputId": "7bef3e93-a149-4504-c72f-892a02ce4664"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.369600385427475\n",
      "Test accuracy: 0.8907672166824341\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(X_test, Y_test, batch_size=64, verbose=0)\n",
    "print('Test loss:', score[0])\n",
    "print('Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XJOCI3OhOZjU"
   },
   "source": [
    "### Make Prediction on the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RZUvb8pVOZjU",
    "outputId": "d2006b58-9955-4c32-b6c9-8cbc728a01a6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13/13 [==============================] - 1s 19ms/step\n"
     ]
    }
   ],
   "source": [
    "Y_test_pred=model.predict(X_test, batch_size=64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zvgO9El9QfrW",
    "outputId": "707e96ed-ee0e-4f58-8d0e-7d66f217588c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.6702510e-04, 9.9818128e-01, 5.3442138e-05, 2.7706409e-07,\n",
       "       1.0979177e-03], dtype=float32)"
      ]
     },
     "execution_count": 347,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test_pred[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_epAiYquOZjV",
    "outputId": "36e1beb1-2bd1-4e78-f795-61890f876d58"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 348,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(Y_test_pred[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Gb6ZtDyDOZjV",
    "outputId": "f8404488-986a-4194-c096-d1a35b72aa66"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {
    "id": "MAt-AFp1wKhU"
   },
   "outputs": [],
   "source": [
    "Y_test_pred=np.argmax(Y_test_pred, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ql1Ui3ekv92b",
    "outputId": "6986a16a-96b8-49d3-f17e-5b8fff0e59be"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.79      0.83       176\n",
      "           1       0.90      0.90      0.90       154\n",
      "           2       0.85      0.88      0.87       146\n",
      "           3       0.86      0.97      0.91       122\n",
      "           4       0.96      0.94      0.95       171\n",
      "\n",
      "    accuracy                           0.89       769\n",
      "   macro avg       0.89      0.90      0.89       769\n",
      "weighted avg       0.89      0.89      0.89       769\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(Y_test, Y_test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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